# Insurgency as Complex Network:
Image Co-Appearance and Hierarchy in the PKK

Ollie Ballinger
Department for International Development
Oxford University
<EMAIL_ADDRESS>

###### Abstract

Despite a growing recognition of the importance of insurgent group structure
on conflict outcomes, there is very little empirical research thereon. Though
this problem is rooted in the inaccessibility of data on militant group
structure, insurgents frequently publish large volumes of image data on the
internet. In this paper, I develop a new methodology that leverages this
abundant but underutilized source of data by automating the creation of a
social network graph based on co-appearance in photographs using deep
learning. Using a trove of 19,115 obituary images published online by the PKK,
a Kurdish militant group in Turkey, I demonstrate that an individual’s
centrality in the resulting co-appearance network is closely correlated with
their rank in the insurgent group.

##  1 Introduction

The organizational structure of a militant group governs many of its
fundamental characteristics including its robustness to different
counterinsurgency strategies, ability to pursue negotiated settlements, and
factionalization. Though the most widely cited scholarly analyses of conflict
largely treat insurgent groups as unitary actors (Fearon and Laitin, 2003;
Collier and Hoeffler, 2004; Hoeffler and Rohner, 2013; Cheibub et al., 2009;
Blattman and Miguel, 2010), a growing body of literature seeks to understand
the complex social and political organization of these groups through the
application of concepts and methods from social network analysis (SNA). The
main impediment to this type of research is data availability: in a review of
the literature on SNA in the study of insurgency, (Zech and Gabbay, 2016, p.
231) conclude that “The covert nature of militant groups makes them a
difficult subject to study in general, a problem that becomes particularly
acute when the objective is to map out the very internal structure that
militants go to great lengths to conceal.” In this paper, I introduce a new
methodology that leverages deep learning and unsupervised clustering to
automatically generate a social network graph based on co-appearance in
photographs– an abundant but underutilized source of data often generated by
militants themselves. I then demonstrate a close relationship between the
topology of the resulting graph and the organizational structure of the
insurgent group, whereby high-ranking militants are more central in the image
co-appearance network.

Though insurgent groups generally produce very little public data, there is
one main exception; online visual propaganda is the cornerstone of most
militant groups’ recruitment strategies, with content ranging from combat
footage, to magazines, to social media posts (Baele et al., 2020; Dauber and
Winkler, 2014). Over a five month period in 2007, U.S. forces in Iraq raided
eight Al-Qaeda media labs and seized 23 terabytes of images and videos
destined to be uploaded to the internet (Dauber, 2009). More recently,
militants have taken to directly posting these images on social media
themselves (Klausen, 2015); even loosely knit movements such as the
participants in the Capitol Hill riots often document their activities on
platforms such as Parler, generating over 70 terabytes of image and video
data. Photographs– particularly those in which individuals appear with each
other– are a rich source of relational data and are plentiful in the context
of most insurgencies. Though image co-appearances have been used to construct
social networks of university students and wedding-goers (Lewis et al., 2008;
Berry, 2006), they have never been used to construct the social networks of
militants. Thus, I seek to answer the following question: To what extent can
co-appearance in militant photographs be used to understand the social,
political, and organizational forces that structure an insurgent group?

The following analysis uses as its only input 20,000 publicly available images
published online by the Kurdistan Workers’ Party (Partiya Karkerên Kurdistanê,
henceforth PKK), an insurgent group primarily active in Turkey. This
unstructured image data is converted into a social network graph in three
steps: faces are automatically extracted from images using deep learning,
nodes are generated by identifying individuals across images via unsupervised
clustering, and edges are formed by linking individuals who appear together in
photographs. Because “posing in a photograph with others is a deliberate act,
and is generally indicative of some social connection” (Golder, 2008, p. 44),
this process effectively measures the degree and nature of an individual’s
social embedding within the rebel group. The result is the PKK Image Co-
Appearance Network (PICAN), a social network graph that mirrors many known
characteristics of PKK. The PICAN has a number of useful analytical properties
including a strong relationship between node centrality and rank, as well as a
temporal dimension extracted from image metadata. These enable the
identification of political factions within the PKK, a better understanding of
the group’s resilience to counterinsurgency tactics, and even insights on
gender segregation among militants. Ultimately, this analysis demonstrates
that the abundance of unstructured image data generated by insurgents can be
harnessed to generate social network graphs that deepen our understanding of
the organizational structure of militant groups.

This paper proceeds as follows. Section 2 reviews the literature on the
application of network analytic methods to insurgent groups. Section 3
describes the image dataset derived from online PKK obituaries and discusses
the performative nature of the photographs therein and their implications for
the resulting co-appearance network. Section 4 outlines the three step
methodological approach: extracting faces from images, clustering individuals’
faces across images, and generating a network based on co-appearance. The rest
of the paper analyzes the properties of the resulting network: Section 5 shows
that a qualitative interpretation of different measures of centrality enables
a rudimentary distinction between members of the political and military wings
of the PKK, and even captures the marginalization of key figures following
failed leadership struggles. Section 6 demonstrates a robust relationship
between node centrality and rank within the PKK by cross-referencing nodes in
the PICAN with wanted persons lists maintained by the Turkish government.
Section 7 analyzes the relationship between network structure and the outcomes
of various counterinsurgency tactics, with particular focus on the group’s
regrowth following a military defeat. Section 9 concludes the paper.

##  2 Literature Review

Though insurgent groups come in all shapes and sizes, the scholarly endeavor
to understand the causes and conduct of civil conflict has largely ignored the
internal characteristics of the actors involved. Sanín and Giustozzi (2010)
illustrate the importance of these organizational factors using two examples:
the FARC– a highly centralized "peoples’ army" divided into hierarchically
organized military units–and the Taliban, a loose confederation of semi-
autonomous factions organized along tribal lines. Key outcomes in both of
these conflicts are directly tied to group structure, including the
feasibility of negotiated settlements; successive peace deals in Afghanistan
failed in no small part due to the need to satisfy a diverse array of factions
within the Taliban (such as the powerful Haqqani Network), as well as the
individual proclivities of their leaders (E. Neumann, 2014). In contrast, the
2016 peace accord with the FARC was facilitated by the fact that "leadership
had to convince its membership—something much easier in a highly hierarchical
organization such as the FARC-EP" (Segura and Mechoulan, 2017).

The application of concepts and methods from Social Network Analysis (SNA) to
the study of insurgent movements has generated a number of theoretical
propositions related to the topology of militant networks. Eilstrup-
Sangiovanni and Jones (2008) argue that decentralized networks such as Al-
Qaeda and the Taliban are flexible and adaptable, but face challenges with
internal cohesion and decision making that can make them prone to excessive
risk-taking. Stohl and Stohl (2007) contend that many ethnicity-based
insurgencies such as Hamas, Hezbollah, and ETA form more centralized
structures: small-world, scale-free networks which are characterized by short
degrees of separation between nodes, well-connected hubs, and a degree
distribution that follows a power law. These types of networks are
particularly robust to the removal of nodes at random because only a small
number of nodes are responsible for the overall connectivity of the network
(Verma and Zhang, 2020). Due to difficulties inherent to the study of dark
networks, “Empirical studies have mostly not engaged the central concerns of
the theoretical literature—the relative advantages of centralized versus
decentralized networks, the relationship between operational capability and
network density, and the relevance of scale-free degree distributions” (Zech
and Gabbay, 2016, p. 231). In the remainder of this section, I highlight the
ways in which the current approach can help address three key challenges
evident in the empirical literature: data availability, selection bias and the
use of secondary sources, and ambiguous relational ties.

###  2.1 Data Availability

The primary impediment to the application of SNA to the study of insurgent
groups and other dark networks is that these organizations are often
“purposefully attempting to remain opaque” (Morris and Deckro, 2013). Yet
virtually every insurgent group maintains some form of online presence, and in
most contexts their use of visual propaganda has eclipsed other media in their
public relations (Dauber, 2009). Images have fast become one of the main
sources of data for empirical analyses of individual insurgent groups;
researchers have used the vast quantities of image data generated by insurgent
groups to better understand their ideological foundations (Baele et al.,
2020), shifts in their recruitment strategies (Lakomy, 2019), and processes of
collective identity formation (Macdonald, 2021). At the same time, a number of
studies have used image co-appearance to construct social networks in other
contexts, recognizing that valuable information on social relations can be
gleaned therefrom. However, these have either required manual coding (Berry,
2006; Golder, 2008), or some level of structure in the image data such as the
tagging of individuals in Facebook photos (Lewis et al., 2008). In the context
of militant photographs, image data is almost certain to be unstructured and
the manual recognition and tagging of thousands of unknown individuals is
unfeasible.

By leveraging facial recognition technology and unsupervised classification to
automate the generation of an image co-appearance network, the approach taken
herein is far more scalable than manual coding, and can utilize completely
unstructured image data. Both of these features are critical given that image
data in the context of insurgent groups are likely to be both voluminous and
unstructured, with basic information including the number of individuals
contained in the photographs likely to be unknown. Furthermore, the image
processing pipeline allows for the collection of a rich array of node-level
attributes through the extraction of image metadata including location, date,
camera type/serial number, as well as apparent age and gender estimation using
deep learning. The code used to turn unstructured image data into a co-
appearance graph has been published as an open source Python package. Using
three functions, researchers can easily extract faces from images, cluster
faces based on similarity, and generate a graph based on co-appearance. The
package features a flexible implementation that can be optimized according to
the nature of the image data at hand; this includes options to extract
metadata and perform apparent age and gender estimation, enabling attribute-
based analysis (e.g. homophily, ERGMs, clustering) and where datetime metadata
are available, analysis of the network’s evolution over time.

###  2.2 Selection Bias and Secondary Sources

Due to the extreme scarcity of primary source data generated by covert
organizations, the vast majority of existing SNA studies of insurgent groups
rely on secondary data, including newspaper articles (Rodríguez, 2005;
Metternich et al., 2013; Krebs, 2002), legal documents (Jordan et al., 2008),
and public statements (Magouirk et al., 2008). This is not only time
consuming– thereby limiting scalability and resulting in smaller sample
sizes–it introduces a measure of subjectivity into the analysis. (Koschade,
2007, p. 567) social network analysis of the Jemaah Islamiyah cell responsible
for the Bali bombings in 2002 contains only 17 nodes, connected on the vague
basis of having had “numerous weak contacts over the period in question” or
having resided together. This requires significant prior knowledge of the
actors involved, limiting the novelty and utility of insights gleaned from the
analysis. It also imposes an extreme degree of selection bias that effectively
precludes the inclusion of anyone below the highest echelon.

Selection bias is generally less severe in the rare studies that rely on
primary data. Using a trove of thousands of ISIS job applications, Edgerton
(2022) is able to study suicide bomber mobilization and kin/peer ties at the
level of individual recruits. Indeed, using ego networks constructed through
interviews, Stys et al. (2022) found that even complete data on "covert" group
membership neglects important actors, as the liminal space between the rebel
group and society is populated with individuals (e.g. demobilized combatants)
who can act as brokers or fulfill other important roles. The use of
photographs does not fully solve the issue of selection bias: some individuals
may be systemically important but camera shy, while others may simply be
photogenic. The extent of this bias depends on the context in which pictures
are taken, and is discussed further in the following section. However, social
networks created using image co-appearances are likely to yield a far more
complete picture of a militant group than those created with secondary
sources; lower level commanders and foot soldiers are unlikely to appear in
news articles and legal documents, but may well appear in a group photograph,
footage from a patrol, or pictures of a ceremony. Thus, a key advantage of the
present approach is that it utilizes primary data in the form of images
published by militant groups, and can yield a more complete picture of an
organization.

###  2.3 Ambiguous Relational Ties

A significant methodological concern in the application of network analytic
methods to the study of militant groups is “the use of implicit, poorly
characterized relational ties in many studies. Some analyses use relational
data coded to cover a wide range of social exchange” (Zech and Gabbay, 2016,
p. 231). In a study of the Provisional Irish Republican Army (PIRA), Gill et
al. (2014) create a network of relational ties between individuals including
marriage, friendship, blood relation, and co-involvement in PIRA activities.
Even within relational ties of the same type (friendship, kinship,
cohabitation), there can be substantial variation in the weight of the tie
that is rarely accounted for (Pedahzur and Perliger, 2006). If the ties
between nodes encompass a diverse range of social interactions, network
statistics and hypothesis tests derived from an aggregate quantitative
analysis of these edges lose conceptual clarity.

Co-appearances in images have been used to construct social networks in
several influential studies, and are generally recognized as signalling
significant social ties between individuals. Berry (2006) argues that the use
of co-appearances in wedding photographs provides a less subjective measure of
interracial friendships than traditional measures thereof including surveys.
In the context of facebook photos, Lewis et al. (2008) suggest that co-
appearance reflects a relatively high level of positive affect between the
individuals involved, as well as a desire to be socially recognized with the
individual. Though the process underlying when and where militants take
pictures is governed by specific social practices (e.g. training academy
graduations, platoon photographs, celebrations), co-appearance acts as a
reliable proxy for the existence of personal contact between individuals.
This, in turn, plausibly measures the extent of an individual’s social
embedding within an organization: if the same person is persistently present
in group photographs in a militant organization, there is likely to be a
reason.

Figure 1: A Photoshoot with the PKK’s Leader

These images were taken between 07:24 and 12:41 on 05/05/2014, using a Canon
EOS 70D with the serial number “43021010637”. They depict Murat Karayilan, the
current leader of the PKK, indicated by the red box. They also feature Ekrem
Güney, a mid-level commander, indicated in yellow.

##  3 Data: Militant Photographs

When a member of the PKK is killed, an entry for the individual is added to
the group’s obituary website. Each obituary features photographs of the
recruit, including a high definition portrait. These photographs appear to
have been taken for a wide variety of reasons. The most common type of
photograph is a posed portrait of the fallen militant. Others feature
militants posing next to a commander. Many appear to be taken at various
ceremonies and celebrations including funerals, training academy graduations,
or during Nowruz. Photographs are not taken during executions of suspected
traitors, nor are those who suffer this fate given the honor of an obituary.
As such, this image dataset represents a collection of the moments that PKK
members and photographers have decided to capture, and the social interactions
between the individuals therein. This section explores different aspects of
this performance and how they are likely to manifest themselves in a co-
appearance network.

###  3.1 Posed Photographs

19,115 images were downloaded from 2484 individual obituaries dated between
2000 and 2021. An interactive visualization generated using the entire image
dataset used herein allows readers to qualitatively explore the photographs.
The selection of many of these moments and individuals are doubtlessly curated
to present an “idealized front” which furthers particular narratives about the
PKK. For example, the representation of women in these images is not only a
manifestation of the PKK’s feminist ideological tenets, but is an integral
part of the group’s public relations and recruitment strategies. Wood (2019)
notes that “The image of a smiling young woman holding an assault rifle has
become a common feature of media reports about the Partiya Karkerên Kurdistanê
(PKK)”. Indeed, women are slightly over-represented in these images: on
average, women appear in 6.1 images, while men appear in 5.2. A T-Test
suggests that this gendered difference in mean photograph appearances is
significant (t=5.18; p<0.001). Because women appear in more photographs than
men, they are likely to appear more central in the co-appearance network.

Similarly, images of insurgent leaders are inherently performative. Though a
rebel group may want to avoid exposing their command structure in photographs,
images of the leaders amongst the rank and file rather than cowering in caves
serve to cast them as both fearless yet humble. If this were the case, we
could reasonably expect a relationship between an individual’s seniority and
the number of co-appearances: a foot soldier may only appear with one or two
commanders, but a commander would appear with many foot soldiers. However, the
ways in which hierarchy is discernible from these images may not be consistent
at all levels of the command structure; despite being a pivotal figure in the
Cuban Revolution, Juan Almeida Bosque’s likeness is nowhere near as ubiquitous
as that of Che Guevara. Individuals who are systemically important but not
particularly salient could be under-emphasized in an image co-appearance
network.

The six images in Figure 1 illustrate how the portrayal of leaders as fearless
and humble generates a positive relationship between image co-appearance and
rank. These images were taken between 07:24 and 12:41 PM on May 5th, 2014,
using a high-end camera (Canon EOS 70D) with the serial number “43021010637”.
They were the only images taken with this camera.

An older man is present in all six pictures. In the top three photos he poses
with the same man (who appears to be in his 50s), including one in which they
are seated with a third man, sharing tea and talking. In the three remaining
photos, he poses next to a different young man each time. The older man
present in all six images is Murat Karayilan, the 67-year-old current leader
of the PKK. The middle-aged man is Ekrem Güney, a mid-level commander who was
killed in 2016. The three young men appear to be foot soldiers. A co-
appearance network generated from these images would see Karayilan with seven
edges, Güney with four, and the foot soldiers with one edge each. This type of
posing links rank with co-appearance in such a way that it not only separates
the leadership from the infantry, but correctly orders the leadership in terms
of rank. Though these images appear to be from a photoshoot– all taken with
the same camera within hours of each other–this general principle reasonably
extends to other social practices captured in these photographs in which
posing is common, including ceremonies and holidays.

###  3.2 Candid Cameras

Though many of these images are staged and curated to advance certain
narratives of the PKK, the majority appear to be relatively candid. There are
two likely reasons for this. The first is that paradoxically, candid pictures
in this context make for better propaganda. In a study of online
representations of martyrdom during the Arab Spring, Halverson et al. (2013)
found that the depiction of martyrs as being relatable was crucial in
generating the “imagined solidarities” that translate the collective
identification with the martyr into support for a social movement. As an
online repository of PKK obituaries, the emotive force that these images draw
on is empathy– they depict seemingly ordinary people eating meals together,
playing backgammon, laughing; ordinary people who were killed by the Turkish
state. Because empathy relies on one’s ability to place oneself in an other’s
position, candid images in this context are far more affective than staged
propaganda photos.

The second reason many of these images appear candid is that the PKK publishes
more traditional and overt propaganda elsewhere: not only is this obituary
website not the primary way in which the PKK disseminates propaganda to the
wider world, it’s not even the PKK’s main website (hpgonline.com). The group
also publishes several magazines (Serxwebun and Berxwedan), maintains social
media accounts on various platforms, and even broadcasted an international
satellite television channel (Roj TV) which has been replaced by an online
streaming platform (Gerîla TV). The PKK disseminates a wide range of content
on these platforms, ranging from combat footage to poetry. Thus, while the PKK
makes extensive use of online visual propaganda to portray itself to a public
audience, this is not the primary function of the obituary website from which
the images used in this study are derived.

Figure 2: Cameras Used by the PKK

The Y axis shows the number of pictures taken with each type of camera. The
plurality are taken with a high-end cameras, but the majority are taken with
cheaper models.

Metadata extracted from these photographs contains the make and model of the
cameras employed by the PKK, supporting the proposition that many of these
images were taken by amateurs. Figure 2 shows the number of photographs in the
sample taken with different types of cameras. Though the plurality of these
images were taken using professional DSLR cameras such as the Nikon D90 or the
Sony DSC-H1, the majority were taken with cheaper digital cameras accessible
to amaterus such as the Sony DSC-W30 and W2000. The notion that many of the
images taken with these cameras are “candid” is not meant to imply
objectivity– the deliberate presentation of candor is itself a form of
performance. Rather, it simply emphasizes different actors and social
relations: this style of photography likely increases the embeddedness of low-
ranking individuals in the network by including group pictures of friends and
comrades.

##  4 Methodology

Following collection, the unstructured image data is converted into a social
network based on co-appearance in three main steps. First, I use a Residual
Neural Network to extract facial encodings from images. I then use a graph
clustering algorithm to identify individuals across images, generating a
unique cluster of faces for each individual. Finally, I construct a weighted
graph in which each cluster is a node, and each co-appearance constitutes an
edge. These three steps are illustrated in Figure 3.

Figure 3: Automatic Image Co-Appearance Network Creation

There are three steps in the automatic generation of an image co-appearance
network; extracting faces from images, identifying the same face across
images, and linking faces based on co-appearance

###  4.1 Extracting Faces

The first step in this analysis involves extracting faces from images. The
approach taken herein relies on the FaceNet system developed by Schroff et al.
(2015), which converts face images to a “compact Euclidean space where
distances directly correspond to a measure of face similarity”. These
128-dimensional face embeddings can be used to store, compare, and cluster
faces based on similarity. A pre-trained Residual Neural Network with 29
convolutional layers was used to detect faces, generate embeddings, and
perform apparent age and gender estimation. The model was accessed via the
Python distribution of Dlib, which reaches an accuracy of 99.38% on pairwise
facial recognition using the standard “Labeled Faces in the Wild” (LFW)
benchmark dataset (King, 2009).

In total, 23,496 faces were extracted from 19,115 images. Cropped face image
tiles are generated to enable manual verification, and facial embeddings are
stored for clustering. Inspection of face tiles reveals a number of
interesting artifacts, including the extraction of artistic representations of
Abdullah Öcalan in the form of flags, paintings, and even lapel pins; Samples
of these clusters are provided in the appendix. These are discarded.

###  4.2 Clustering Faces

Once faces are extracted, they are clustered based on similarity to identify
individuals across images. The accuracy of the clustering process is assessed
through the construction of a labeled subset of the data. Though images from a
given obituary will primarily contain the face of the deceased individual,
they also frequently contain group photos in which other faces are present.
However, if an image from an obituary only contains one face, this is almost
always a portrait of the obituary’s subject. Thus, by restricting the sample
of images to only those that contain one face, a labeled subset of the data is
created.

The choice of the clustering algorithm is informed by a number of constraints
in the data. Because some of the people who appear in the images are still
alive, the number of unique individuals (and therefore, clusters) is unknown,
precluding the use of algorithms such as k-means or spectral clustering. A
number of unsupervised clustering approaches have been applied to the task of
face clustering in recent years, including among others Agglomerative
Hierarchical Clustering, Density-Based Spatial Clustering of Applications with
Noise (DBSCAN), and Graph-Based Clustering. Graph-based clustering has
recently been shown to achieve high accuracy in face clustering tasks (Chang
et al., 2019). This approach begins by creating an undirected weighted graph
$G=(V,E)$ in which each vertex (V) is a facial embedding and each edge (E) is
weighted according to the euclidean distance between vertices. In the
construction of this input graph, only edges whose weights are below a certain
euclidean distance cutoff are included. The optimal cutoff parameter is
selected based on the results of tuning shown in Figure 4.

Figure 4: Unsupervised Clustering Accuracy

Effect on accuracy of tuning the euclidean distance parameter in graph
clustering. The optimal threshold is 0.39

Once the input graph is created, the Chinese Whispers (CW) algorithm is used
to partition the nodes into clusters. CW functions by first assigning a unique
class to each node. Through an iterative process, each node inherits the
strongest class in its local neighborhood, which is the class with the highest
sum of edge weights to the current node (Biemann, 2006). The CW algorithm
achieves a remarkable degree of accuracy in the current dataset.

Maximum accuracy is achieved when the euclidean distance threshold is set to
0.39. If the cutoff is too low, false negatives increase as faces are not
sorted into clusters. If the cutoff is too high, the number of false positives
increases. The Rand Index, which measures the similarity between a set of
labeled and predicted clusters, achieves a value 95.6% under optimal
conditions. The Adjusted Mutual Information index provides a similar
assessment of clustering performance, but is more robust to unbalanced data
with both very large and very small clusters; it achieves a value of 97.4%
under optimal conditions, with 80.3% of face tiles sorted into clusters. The
relatively high proportion of faces that are not sorted into clusters largely
reflects poor image quality under certain conditions; large group photographs
where some individuals are in the distance, low resolution images, or those
taken with old cameras present a challenge to facial recognition.

The quantitative accuracy statistics provided above are enabled by the fact
that images are sorted by obituary. As a final step, a mosaic image composed
of all of the face tiles in a given cluster is created, enabling a visual
assessment of clustering performance where image data are completely
unstructured. Figure 5 shows an example of the face tile mosaic generated for
Bahoz Erdal, the leader of the PKK’s armed wing. The clustering process
manages to identify him across an impressive range of lighting conditions,
pose variations, image resolutions, ages, and hair styles without any false
positives.

Figure 5: Auto-Generated Cluster for HPG Commander Bahoz Erdal

The clustering algorithm identifies Erdal in 72 unconstrained images. He is
identified across a number of different lighting conditions and age ranges.

###  4.3 Generating a Network

Having identified individuals across multiple images, a network $G=(N,E)$ is
generated wherein each node N is an individual, and each edge E connecting
nodes indicates that the individuals appeared in a photograph together. The
PKK Image Co-Appearance Network (PICAN) is weighted, meaning that an edge
linking two vertices will take on the value of the number of co-appearances
between the two individuals. For example, if individuals a and b appear
together in one photograph, edge $(a,b)=1$. If individuals c and d appear
together in 10 photographs, edge $(c,d)=10$. The degree $k_{i}$ of node i
reflects the number of edges linking that node to others. These edges are
undirected.

Figure 6: PKK Image Co-Appearance Network (PICAN)

This figure displays a co-appearance network generated from nearly 20,000
images published online by the PKK. Each node is a cluster of faces
corresponding to an individual (see Figure 4). Edges link nodes if the
individuals appear together in a photograph, and are weighted by the number of
co-appearances. Nodes are sized based on the number of pictures, and colored
according to the number of co-appearances.

In Figure 6, a node’s size is dictated by the number of images of an
individual, while the node’s color reflects the number of co-appearances. The
network has 2999 nodes and 991 edges. A large number of these nodes are
isolates, meaning that they have no edges (k=0), making up the ring of blue
nodes around the graph. These isolates derive from obituaries in which there
are either a small number of images or which only contain portraits. The
largest connected component (LCC) comprises 491 nodes, and is represented as
the densely connected object in the center of the graph. Though there are also
a number of smaller connected components, subsequent analysis will focus
primarily on the LCC. Having generated the co-appearance network, the rest of
this paper analyzes the properties of the PICAN in reference to external
information about how the PKK is organized.

##  5 Functional and Factional Divisions

Figure 7: PKK Leaders: the 15 Most Central Nodes

The 15 most central nodes in the PICAN correspond to known leaders of the PKK.
Different measures of centrality shed light on the nature of their image co-
appearances, which correspond to functional and factional divisions within the
PKK.

The performative nature of the photographs enables a rich qualitative
interpretation of network statistics. Degree centrality simply measures the
number of neighbors that a node has, which in this case is simply the number
of times they appeared in photographs with others. Eigenvector centrality
takes into account how well connected a node’s neighbours are. A node with
relatively low degree can have high eigenvector centrality if they are
connected to well-connected nodes(Golbeck and Klavans, 2015). As such,
eigenvector centrality is often interpreted as a measure of popularity in
social networks (Borgatti et al., 2018). Betweenness centrality measures the
fraction of shortest paths that pass through a given node (Jia et al., 2012).
Nodes with high betweenness centrality occupy a powerful structural position
in the network as they function as a “broker or a bridge” between subgroups
and the rest of the network (Hansen et al., 2011).

There are multiple mechanisms through which node degree in an image co-
appearance network could act as a proxy for seniority. The most parsimonious
explanation is that most high-ranking members of the PKK have simply been
active in the field for longer. The longer an individual is active, the more
opportunities they have to appear in images with others. Another likely reason
linking node degree and rank relates to the occasions in which pictures are
taken. As previously discussed, many photographs are taken at ceremonies in
which high-ranking members are present such as training academy graduations.
New recruits often pose with commanders during these ceremonies, thereby
generating a large number of edges in the PICAN.

Figure 7 displays the 15 most central nodes in the PICAN using all three
measures, ordered by betweenness centrality. There are three nodes that stand
out regardless of which measure of centrality is employed: Bahoz Erdal, Murat
Karayilan, and Duran Kalkan. The node with the highest betweenness centrality
in the PICAN is that of Bahoz Erdal, who has commanded the PKK’s armed wing,
the HPG (Hêzên Parastina Gel) since its founding in 2004. Despite his
seniority in the PKK’s military apparatus, he is not a member of the PKK’s
Executive Committee. Images of Erdal date back decades, and he appears to be
primarily posing with rank and file militants; he is frequently the only
connection for many of his neighbouring nodes. As the PKK’s top military
commander, his ubiquity in pictures taken in the field is to be expected.
Though Karayilan has a slightly lower betweenness centrality than Erdal, his
eigenvector centrality is more than twice as great. Karayilan is the current
leader of the PKK, and frequently appears alongside other members of the PKK’s
executive council and top commanders as well as rank-and-file members. Unlike
Erdal, many of his neighbours are also high-degree nodes rather than rank-and-
file members, inflating his eigenvector centrality. The same is true of Duran
Kalkan, who is also a member of the Executive Committee. Intuitively, those
who appear most frequently with others tend to be high-ranking individuals.
However, a closer examination of those that do not have many co-appearances
despite their high rank demonstrates that internal political discord is
organically reflected in these photographs.

A particularly interesting node in this regard is that of Cemil Bayik, one of
the founding members of the PKK and one of the three current members of the
PKK’s Executive Committee alongside Karayilan and Kalkan (Stansfield and
Shareef, 2016). He was considered the PKK’s “longtime number two” after
founder Abdullah Öcalan, and was the top commander of the PKK’s former armed
wing, the ARGK (Artêşa Rizgariya Gêle Kurdistan) (Gunter, 2000). Despite being
a central figure in the PKK’s development, Bayik’s node in the PKK network is
far less central than those of his peers in the Executive Committee, Karayilan
and Kalkan. His eigenvector centrality is particularly low– the lowest in the
sample above, which is surprising given that the other Executive Committee
members have the highest eigenvector centrality in the network by virtue of
frequently posing with other high ranking individuals. Indeed, in this sample
of nearly 20,000 photographs spanning decades, Bayik– one of the highest
ranking members of the PKK on paper– does not appear in a single photograph
with any of the other top leaders of the organization, including the two other
members of the Executive Committee.

Figure 8: PKK Leadership in the PICAN LCC

The position of four senior members of the PKK are shown in the PICAN.
Karayilan, Kalkan, and Bayik are the three members of the PKK’s Executive
Committee. Erdal is the leader of the PKK’s armed wing, the HPG.

This discrepancy appears to be the result of a failed leadership struggle that
occurred during the PKK’s reorganization following its military defeat in
1999. The PKK dissolved the military wing led by Bayik (the ARGK), and
replaced it with a new wing called the HPG, led by Bahoz Erdal. Indeed, “when
Bahoz Erdal became the head of the PKK’s military wing in June 2004, Bayik
apparently found himself in a lesser position.” (Gunter, 2014, p. 65). The
reasons for Bayik’s ouster were likely related to his fraught relationship
with the PKK’s founder; “Öcalan has criticized Bayik’s military leadership and
ability to direct a guerrilla movement. According to Öcalan, Bayik prefers to
stay behind the frontlines and has become involved in several controversial
cases. For instance in 1992, fearing capture by the Turkish security forces,
Bayik killed 17 wounded PKK militants in a cave.” (Uslu, 2008). When the PKK
resumed military operations in 2004, Bayik was consigned to diplomatic duties
and spent much of his time in Iran, far from the battlefield. As a result,
Bayik simply appears in far fewer photographs than his peers in the Executive
Council.

People who frequently appear with others in these photographs tend to be high
ranking. Indeed, the strength of the relationship between how socially
embedded an individual is (i.e. their centrality in the network) and their
influence within the PKK is evidenced by the fact that the PICAN even captures
the effect of internal political struggles; the discrepancy between Cemil
Bayik’s status as a member of the PKK’s Executive Committee and his relatively
low centrality in the PICAN reflects his marginalization following a failed
leadership struggle. Rather than being a source of bias, the performative
nature of photography in the context of militant photographs is itself a rich
source of contextual information that can be interpreted through an
examination of the nature of the co-appearances.

##  6 Node Centrality and Rank

Table 1: Summary Statistics: Matching PICAN Nodes with Government Watchlists

|  | Red | Blue | Green | Orange | Grey
---|---|---|---|---|---|---
Panel A: | Reward (TL, 000) | 10000 | 3000 | 2000 | 1000 | 500
Matching Statistics | Listed Individuals | 178 | 49 | 73 | 158 | 581
| Matched | 28 | 9 | 6 | 20 | 59
| % Matched | 15.7 | 18.4 | 8.2 | 12.7 | 10.2
Panel B: | Image Count | 14.17 | 12.17 | 3.0 | 7.67 | 6.95
Network Centrality | % Isolates | 13.79 | 58.33 | 71.43 | 85.71 | 84.48
| Degree | 0.0235 | 0.0068 | 0.0014 | 0.0014 | 0.001
| Eigenvector | 0.0629 | 0.0332 | 0.0102 | 0.0008 | 0.0071
| Betweenness | 0.0571 | 0.0222 | 0.0 | 0.0017 | 0.0015

  * •

The columns in this table indicate the colors of five wanted persons lists
maintained by the Turkish Government. Panel A reports the value of the reward
offered for individuals on each list, the number of listed individuals, and
the number and proportion of PICAN nodes that were matched with these
individuals. Panel B reports attributes for matched nodes in each list,
including the average number of images they appear in, the proportion that are
isolates, as well as three measures of centrality.

The previous section established that image co-appearance can highlight the
top leadership of a rebel group, and even shed light on political divisions
therein. However, the link between photogenicity and rank may only exist for
the highest ranking (and thus, most salient) members of an insurgent group:
while new recruits may clamor for a photograph with the supreme leader, a
platoon commander may inspire less enthusiasm. This section matches 126
individuals from wanted lists maintained by the Turkish government with nodes
in the PICAN to test whether there is a relationship between an individual’s
rank within the PKK (as measured by the reward offered for their capture) and
their corresponding node’s position in the PICAN. Linear regression models
suggest that over half of the variation in the monetary reward offered for an
individual can be explained by image co-appearances alone. Exponential Random
Graph Models show that higher ranking individuals are significantly more
likely to form ties in the PICAN. These results are not simply driven by the
top leadership posing with new recruits; In both sets of models, the
relationship between seniority and centrality is robust to the exclusion of
the 28 highest-ranking officials (those on the Red List), suggesting that
photogenicity gradually increases with rank.

The Turkish government maintains lists of wanted persons (including those
killed, captured, and at large), offering monetary rewards for information
leading to their capture. Individuals are assigned into one of five color
coded wanted lists (red, blue, green, orange, and grey), with reward amounts
ranging from 10 million Turkish Lira for individuals on the Red List to
500,000 TL for individuals on the Grey List. The value of the reward offered
for an individual’s capture (i.e. the color of the wanted list that they are
assigned to) is largely a function of the Turkish government’s assessment of
that individual’s rank within the PKK. Article 6 (2) of the “Regulation on
Rewards to be Given to Those Who Help in Discovering Terrorist Crimes” states
that individuals are assigned to one of these five lists by “[…] grouping them
according to their hierarchical position in the terrorist organization and/or
the weight of the consequences of their actions, and by specifying the maximum
amount of reward that can be given to those in each group” (Turkish Ministry
of Interior, 2022). This legislation, available on the wanted website, further
specifies that the highest reward amounts are offered for individuals on the
Red List, who are deemed to be “senior manager[s] in a terrorist
organization”. Though no precise information is given regarding how exactly
individuals are assigned to different lists, individuals on the Blue List seem
to represent the second echelon judging by the relatively small number of
individuals in this category (49), as well as the reward amount (3 million).
The Green and Orange Lists likely correspond to PKK members who are believed
to have some level of managerial responsibility that justifies offering
rewards of 2 million and 1 million, respectively. The Grey List offers the
lowest reward amount and is by far the largest group, with over 500 listed
individuals, which may suggest that they are known to be members of the PKK,
but do not occupy positions of power.

Figure 9: PICAN Nodes Matched with Wanted Lists

In the graph of connected components on the left, nodes in the PICAN are
matched with wanted persons and colored according to the value of the reward
offered for their capture. The bar on the right counts the number of isolates
that were matched at each wanted level. Individuals on the Grey and Orange
Lists– the least wanted categories– tend to be isolates. Individuals on the
higher-priority Red, Blue, and Green lists tend to be well connected.

To examine the relationship between network centrality measures and rank,
individuals from the wanted lists were matched with nodes in the network using
facial recognition and text-based matching. Each entry on the wanted lists
contains a photograph of the individual, their full name, date and place of
birth, and the organization they are alleged to be a member of. Facial
recognition was performed using the individual’s photo on the wanted website,
employing the same euclidean distance threshold (0.39) as the one used for in
the clustering step of the construction of the PICAN network. Text-based
matching was also conducted by identifying exact matches in terms of first
name and last name between both datasets. Both facial and text-based matches
were verified manually, yielding a total of 126 confirmed matches.

Figure 9 shows the position of nodes that were matched with records on the
wanted list, coloring them according to their wanted level. The graph of
connected components on the left shows that individuals on the Red and Blue
lists occupy highly central positions in the network. The bar chart on the
right suggests that very few of these individuals are isolates. Conversely,
individuals on the Grey and Orange lists are largely absent from the graph of
connected components or occupy relatively peripheral positions. The bulk of
these individuals have no co-appearances at all.

The number and relative proportions of individuals identified in each wanted
category are reported in Panel A of Table 1. The generally low match rates can
be attributed to the inherent challenges of identifying members of a
clandestine organization using facial recognition and imagery from two
different sources. In particular, poor image quality posed a significant
challenge for facial recognition– most of the images on the wanted list
website were unusable due to being stretched to fit the dimensions of the
image element, and only one image was available for each individual. Failed
matching due to image quality generates data that is Missing Completely At
Random (MCAR), which simply reduces the sample size. However, differing match
rates between wanted lists raises concerns about selection bias. The highest
match rates were among those in the most wanted categories– 16% of the
individuals on the Red List and 18% of those on the Blue List were matched to
nodes in the PICAN. Lower match rates were observed for the Green, Orange, and
Grey lists, which could indicate that lower-ranking individuals are
underrepresented in the obituary images. However, the lowest-ranking group
(the Grey List) has the largest total number of matches, over twice as many as
the group with the next-highest number of matches.

Panel B of Table 1 reports the average values of several network statistics
for each group. On average, individuals on the Red List appear in 14 photos,
while individuals on the Grey List appear in just 7. The proportion of nodes
in each group that are isolates decreases as the reward amount increases; only
14% of nodes in the Red List were isolates, increasing to 84% in the Grey
List. The same is true of all three measures of centrality. The summary
statistics and the graph in Figure 9 seem to suggest a positive relationship
between node centrality and an individual’s rank within the PKK as determined
by the value of the reward placed on them by the Turkish government.

###  6.1 Linear Models

To further explore the relationship between node centrality and rank, I
specify a simple OLS regression model of the following form:

${Y}_{i}=\beta_{0}+\beta_{1}{x}_{i}+\epsilon_{i}$

Where y is the reward amount (in thousands of Turkish Lira) offered for
individual i, and x is a measure of node centrality. Because measures of
centrality are highly correlated with each other, a separate regression is run
for each metric. Three common measures of centrality are used: degree,
eigenvector, and betweenness. These are standardized by the number of images
an individual appears in to emphasize social embeddedness rather than
photogenicity without introducing multicollinearity between an image count
variable and the centrality measures. For simple degree centrality, this
effectively yields a measure of co-appearances per image. Table 2 summarizes
the results of these regressions.

Table 2: OLS Regression Coefficients for Standardized Network Centrality
Measures and Reward Value

|  Model 1 |  Model 2 |  Model 3 |  Model 4
---|---|---|---|---
(Intercept) |  $2051.32^{***}$ |  $1466.35^{***}$ |  $2442.66^{***}$ |  $1950.80^{***}$
|  $(445.61)$ |  $(269.17)$ |  $(339.06)$ |  $(297.39)$
Image Count |  $113.63^{***}$ |  |  |
|  $(33.00)$ |  |  |
Degree |  |  $3134293.53^{***}$ |  |
|  |  $(258269.53)$ |  |
Eigenvector |  |  |  $352246.85^{***}$ |
|  |  |  $(69378.62)$ |
Betweenness |  |  |  |  $1117667.87^{***}$
|  |  |  |  $(124487.94)$
R2 |  $0.09$ |  $0.54$ |  $0.17$ |  $0.39$
Adj. R2 |  $0.08$ |  $0.54$ |  $0.17$ |  $0.39$
Num. obs. |  $126$ |  $126$ |  $126$ |  $126$
${}^{***}p<0.001$; ${}^{**}p<0.01$; ${}^{*}p<0.05$

  * •

The dependent variable in all models is the value of the reward placed on an
individual by the Turkish Government. The independent variable in Model 1 is
the number of photographs an individual appears in. Models 2, 3, and 4 use
different measures of node centrality divided by the number of images that
individual appears in.

Figure 10: Network Centrality and Wanted Level

The colors represent wanted lists, which offer monetary rewards for an
individual’s capture. They are arranged in descending order of reward value:
10m, 3m, 2m, 1m, and 0.5m. The Y axis represents degree centrality divided by
the number of images an individual appears in. Individuals on the red and blue
lists tend to be far more central than those on the lower-priority lists.

Model 1 regresses the count of images an individual is identified in with the
value of the reward placed on them. Though not a measure of centrality per se,
this provides useful contextual information on whether higher ranking
individuals appear in more photographs than those of lower rank. Indeed, the
results from the first column indicate that for each additional photograph an
individual appears in, the reward amount increases by 114,000 Turkish Lira on
average. However, less than 10% of the variation in reward amounts is
explained by the number of photos an individual appears in. Models 2, 3, and 4
show strong positive associations between degree, eigenvector, and betweenness
centrality and reward values. The model with the greatest explanatory power
relies on simple degree centrality, achieving an $R^{2}$ value of 0.54; in
other words, over half of the variation in the bounty price placed on an
individual can be explained by the frequency with which they appear with
others in photographs. The boxplot in Figure 10 shows the relationship between
standardized degree centrality and wanted level.

To ensure that these results are not entirely driven by extreme values, the
models in Table 2 are re-run excluding the 28 individuals who appear on the
Red List. The top leaders of rebel groups are often well known, but
information on the structure of rebel groups below the level of top leadership
is much harder to come by; second and third order commanders are less
prolific. By excluding the top leadership from the regressions, the results in
Table 3 assess whether node centrality measures not only distinguish the top
echelon from the rank-and-file, but whether they can distinguish lower ranks
as well.

Table 3: OLS Regression Coefficients for Network Centrality Measures and
Reward Value, Excluding Red List

|  Model 1 |  Model 2 |  Model 3 |  Model 4
---|---|---|---|---
(Intercept) |  $672.40^{***}$ |  $913.71^{***}$ |  $932.82^{***}$ |  $901.13^{***}$
|  $(153.90)$ |  $(86.44)$ |  $(87.19)$ |  $(80.83)$
Image Count |  $44.96^{*}$ |  |  |
|  $(17.22)$ |  |  |
Degree |  |  $53811.91^{**}$ |  |
|  |  $(16026.47)$ |  |
Eigenvector |  |  |  $8802.63^{**}$ |
|  |  |  $(3108.03)$ |
Betweenness |  |  |  |  $27185.59^{***}$
|  |  |  |  $(5722.21)$
R2 |  $0.07$ |  $0.11$ |  $0.08$ |  $0.19$
Adj. R2 |  $0.06$ |  $0.10$ |  $0.07$ |  $0.18$
Num. obs. |  $97$ |  $97$ |  $97$ |  $97$
${}^{***}p<0.001$; ${}^{**}p<0.01$; ${}^{*}p<0.05$

  * •

The models in this table are identical to those presented in Table 2, except
that the 28 individuals in the most wanted category (the Red List) are
excluded. Results are robust to this exclusion.

Model 1 shows a substantial decrease in the effect size and significance of
the relationship between the number of photos an individual appears in and
their reward value when the red list is excluded. This reflects some of the
idiosyncrasies of picture-taking in the PKK: foot soldiers can have a
relatively large number of portrait photos, but higher ranking individuals
(particularly those that are still alive) rarely appear alone. Despite
reductions in effect size, significance, and model fit across the board,
Models 2-4 nonetheless display a consistent relationship between node
centrality and wanted level even when the highest echelon is removed from the
analysis.

###  6.2 Exponential Random Graph Models

Though Borgatti et al. (2018) note that hypothesis testing using node-level
attributes is mostly conducted via standard regression models in the
literature, the use of network data violates the assumption that observations
are independent. This problem is overcome by Exponential-family Random Graph
Models (ERGMs), which enable an assessment of the statistical likelihood of
observing certain network configurations, including the influence of nodal
attributes on tie formation. ERGMs treat the observed network (in this case,
the PICAN) as one realization from a set of possible networks with similar
core characteristics (Robins et al., 2007). The general form specifies the
probability of the observed network as a function of a set of network features
that may occur more or less frequently than expected by chance:

$P(Y=y)=\frac{exp(\theta^{\prime}g(y))}{k(\theta)}$

Where $Y$ is the random variable for the state of the network, and $y$ is the
realization thereof. The right hand side specifies a vector of coefficients
$\theta$ for model statistics $g$ of the network $y$, normalized by the sum of
all possible networks $k(\theta)$ (Statnet, 2022). These networks are
generated via Markov Chain Monte Carlo maximum likelihood estimation
(MCMCMLE). If more or less of a certain network configuration is present in
the observed network than in the simulated one, the parameter corresponding to
that configuration is iteratively adjusted in subsequent simulations until the
simulated network closely approximates the observed one (Ellwardt et al.,
2012). For example, given a parameter for the number of edges involving female
nodes, a positive value would indicate that women have a greater propensity to
form ties in the PICAN than in a random graph.

Table 4 reports the results from four ERG Models which assess whether high-
ranking PKK members (i.e., those fetching higher bounties) are more likely to
co-appear in images than lower ranking individuals. Models 1 and 2 utilize the
full PICAN and include a parameter for isolates, while models 3 and 4 exclude
isolates altogether. The inclusion of a triadic close term in the full PICAN
leads to model degeneracy, so it is only included in the analysis of connected
components.

All models converged successfully, indicating that the final simulated network
in each case was sufficiently close to the observed network in terms of the
included parameters. To test the extent to which the Exponential Random Graph
Model presented in Table 4 fits the data, it is convention to observe how well
the model reproduces network properties that are not included as parameters in
the estimation. The main hypothesis being tested in the model is monadic in
nature (whether the nodal attribute of reward value influences the likelihood
of tie formation). As such, similar to Stys et al. (2022), Figure 11 compares
a node-level network statistic– the degree distribution– between the observed
and simulated networks. The simulated networks largely reproduce network
statistics in the PICAN that were not included as terms in the ERGMs,
suggesting that the models faithfully approximate the observed network.

Table 4: Effect of Reward Value on the Likelihood of Edge Formation: Parameter
Estimates from ERGMs

|  Model 1 |  Model 2 |  Model 3 |  Model 4
---|---|---|---|---
Edges |  $-9.69^{***}$ |  $-9.53^{***}$ |  $-7.90^{***}$ |  $-7.55^{***}$
|  $(0.20)$ |  $(0.33)$ |  $(0.16)$ |  $(0.33)$
Isolates |  $2.09^{***}$ |  $2.08^{***}$ |  |
|  $(0.08)$ |  $(0.08)$ |  |
Triadic Closure |  |  |  $1.72^{***}$ |  $1.71^{***}$
|  |  |  $(0.06)$ |  $(0.06)$
Age |  $0.04^{***}$ |  $0.04^{***}$ |  $0.02^{***}$ |  $0.02^{***}$
|  $(0.00)$ |  $(0.00)$ |  $(0.00)$ |  $(0.00)$
Gender |  $0.13^{**}$ |  $0.12^{**}$ |  $-0.00$ |  $-0.01$
|  $(0.05)$ |  $(0.05)$ |  $(0.05)$ |  $(0.05)$
Reward |  $0.14^{***}$ |  |  $0.07^{***}$ |
|  $(0.01)$ |  |  $(0.01)$ |
Wanted (Unmatched) |  |  $-0.08$ |  |  $-0.19$
|  |  $(0.14)$ |  |  $(0.15)$
Wanted (1M) |  |  $0.04$ |  |  $0.09$
|  |  $(0.23)$ |  |  $(0.24)$
Wanted (2M) |  |  $-0.27$ |  |  $-0.73$
|  |  $(0.48)$ |  |  $(0.52)$
Wanted (3M) |  |  $0.59^{**}$ |  |  $0.24$
|  |  $(0.19)$ |  |  $(0.19)$
Wanted (10M) |  |  $1.30^{***}$ |  |  $0.51^{**}$
|  |  $(0.16)$ |  |  $(0.16)$
AIC |  $14129.42$ |  $14133.88$ |  $10476.49$ |  $10471.77$
BIC |  $14196.00$ |  $14253.72$ |  $10529.52$ |  $10567.23$
Log Likelihood |  $-7059.71$ |  $-7057.94$ |  $-5233.24$ |  $-5226.89$
${}^{***}p<0.001$; ${}^{**}p<0.01$; ${}^{*}p<0.05$

  * •

Models 1 and 2 use the full PICAN and include a paramter for isolates. Models
3 and 4 exclude isolates, but include a parameter for triadic closure
(Geometric Edgewise Shared Partners or GWESP). Age and Gender are node-level
attributes. "Reward" is a continuous node-level attribute indicating the value
of reward placed on an individual by the Turkish government. "Wanted" is a
categorical version of this variable, with the reward for each category
indicated in parentheses.

Figure 11: ERGM Goodness of Fit Diagnostics

The empirical distribution is represented as a solid black line, while the
simulated distributions are indicated by the boxplots. Though three very high
degree nodes– the PKK’s main leaders– consistently defy simulation, the rest
of the observed degree distribution is generally within the bounds of the
simulated models.

All models also include apparent age and gender as control variables. Gender
has a modest but significant effect in the full network: women are 1.14 times
more likely to form ties than men, which is consistent with the prior
observation that women appear in more images222$e^{0.13}=1.14$. This likely
reflects the performative nature of many of these photographs, which seek to
emphasize the role of women in the PKK. Age also increases the likelihood of
edge formation, which may result from the fact that older members have simply
had more time to appear in pictures. Alternatively, age could also be proxying
for seniority. Age and rank are likely to be correlated in any military
setting: infantrymen are rarely old, generals are rarely young, and the PKK’s
known leaders are all above the age of 60. Despite this, the “Reward” variable
remains positive and significant in both models, though the magnitude of the
effect is diminished by the addition of controls.

The main variable of interest in all models is the value of the reward placed
on an individual by the Turkish government. Models 1 and 3 include a
continuous version of this variable, denoting the value of the reward offered
for an individual in millions of Turkish Lira. In both the full network and
the restricted sample, nodes corresponding to individuals with a higher reward
value have a significantly higher probability of forming edges. In the full
network, a node on the Red List is 10.92 times more likely to form a tie than
a node on Grey List, ceteris paribus333$(10\times e^{0.14})-(0.5\times
e^{0.14})=10.92$. This effect is halved when isolates are excluded, likely due
to the fact that 85% of the nodes on the Grey list are isolates.

Models 2 and 4 treat the reward variable as categorical rather than
continuous. This enables the estimation of a separate effect for each reward
amount and allows us to set an appropriate base category; though there are
five different wanted lists corresponding to different reward amounts, this
variable contains a sixth category: PICAN nodes that were not matched to
wanted individuals. This constitutes an inappropriate reference group because
information about these individuals’ rank is unknown; as such, each level of
the “Reward” variable is compared against individuals on the Grey list (0.5M
TL). Individuals on the Red and Blue wanted lists (fetching rewards of 10M TL
and 3M TL, respectively) are significantly more likely to co-appear in images
than individuals on the Grey List in the full network. However, the
coefficient on the Blue List category becomes insignificant in Model 4, due to
the exclusion of isolates (and thus over 85% of the observations in the Grey
List reference category). Despite the categorical nature of the variable, the
ratio between the coefficients and the reward amounts of Red and Blue list
members is remarkably close: the reward for an individual on the Red List is
triple that offered for someone on the Blue List, and in both models the
coefficient of the Red List is roughly double that of the Blue List. This
aligns with the OLS results in Table 2, suggesting that co-appearance not only
identifies the top echelon of the PKK’s command structure, but lower ranking
officers as well. However, individuals on the Green and Orange lists (1M and
2M) did not have a significantly higher likelihood of co-appearance relative
to the Grey list, though this is likely due to the small number of
observations in these categories.

The absence of a significant relationship between membership of the Green and
Orange lists and likelihood of edge formation suggest that the relationship
between social embeddedness and node centrality is not consistent across the
full range of the data. Indeed, the lower match rates between individuals on
these two lists and nodes in the PICAN imply that some lower-ranking officers
may be disproportionately absent from the obituary photos altogether.
Nevertheless, results from this section demonstrate a general relationship
between an individual’s rank within the PKK (measured in terms of reward
value) and the centrality of their corresponding node in the PICAN. Results
from the linear regressions indicate that over half of the variation in reward
values can be explained by the frequency with which an individual appears with
others in photographs. All three of the centrality measures vastly outperform
image count in terms of explaining variation in reward values, suggesting that
social embeddedness is a better proxy for rank than photogenicity. ERGM
results are consistent with these findings, with higher value individuals
significantly more likely to form edges in the PICAN. Importantly, this
relationship persists across all models even when the highest-ranking
officials were excluded, meaning that results are not driven purely by the
propensity of new recruits to pose with the group’s leader.

An important caveat to this analysis is that the PICAN is only an
approximation of the PKK’s structure. Co-appearances in these images can
reflect a number of forms of social interaction, from friends sharing a meal
to a new recruit posing with their commander. Edges do not provide any direct
information on the nature of the relationship between two individuals, other
than the fact that they crossed paths. At their most basic level, edges in the
PICAN simply reflect the number of social contacts an individual has had. In
aggregate, the quantity and nature of these interactions correlates strongly
with an individual’s rank, allowing for the creation of a network that
approximates the known structure of the PKK. It is important to note, however,
that some individuals can be important to the organization without being
highly socially embedded or visible in photographs from the field, and
conversely that some highly photogenic individuals may be of low systemic
importance.

##  7 Network Structure and Counterinsurgency Tactics

A significant gap between the theoretical and empirical literature on the
application of SNA to the study of insurgent groups involves the relationship
between network topology and robustness to counterinsurgency strategies
(Helfstein and Wright, 2011). This section seeks to assess the extent to which
a co-appearance network generated from militant photographs can help to fill
this gap.

Despite the strong association between rank and centrality, the PICAN does not
represent an organogram of the PKK; it is a representation of how the PKK
chooses to present itself to the world. This exaggerates certain
characteristics, such as the group’s female cadres, while understating others
such as lower-level commanders, the “dishonorably discharged”, and the
politically marginalized. Nevertheless, given a large enough sample it seems
plausible that an image co-appearance network would capture the general
structural characteristics of an insurgent group– the rough number and
relative proportions of individuals at various ranks, and the relationships
between them. In what follows, I analyse the topology of the PICAN in
reference to historical developments in the conflict between the PKK and the
Turkish government.

Ünal (2016) identifies two main phases in Turkish military strategies towards
the PKK: ground war and targeted killings. Until the year 2000, the Turkish
military largely waged a conventional ground war against the PKK. Under this
doctrine, the Turkish government conducted large scale military operations,
armed roughly 60,000 civilians to act as paramilitary force and burned as many
as 4,000 Kurdish villages to the ground (Gurcan, 2014; Filkins, 2003). Though
this strategy culminated in the PKK declaring military defeat in 2000, it
failed to prevent the group’s resurgence only four years later. Following a
period of low intensity conflict, large-scale hostilities resumed between the
PKK and Turkish forces following the breakdown of a truce in 2015. This new
phase in the conflict has been accompanied by a significant doctrinal shift on
the part of the Turkish government towards a more targeted approach. Rather
than relying on large military operations and poorly trained paramilitaries,
the new approach relies heavily on the use of special forces, drone strikes,
and intelligence gathering (Ünal, 2016). Particularly since 2020, the Turkish
government’s focus has been on targeting the leadership structure of the PKK
(Uslu, 2008).

These various counterinsurgency strategies can be simulated, and their effects
on the network can be assessed empirically. In general, the static robustness
of a network to intentional attacks “analyses the ability of a system to
maintain its connectivity after the disconnection or deletion of a series of
targeted nodes. In this context, the connectivity of the resulting network is
typically measured by the size of the largest connected component (LCC)”
(Lordan and Albareda-Sambola, 2019, p. 1). This section proceeds with an
empirical analysis of the two main counterinsurgency strategies in
chronological order; first, the strategy of inflicting maximum casualties
against the PKK through a ground war, and second, the doctrine of targeted
strikes against high ranking members.

###  7.1 Ground War

Figure 12: PICAN Scale-Free Degree Distribution

The PICAN’s degree distribution (shown in blue) follows a power law of
$k^{-2.58}$, making it a weakly scale-free network.

Much of the historical literature concerning first phase of the conflict
between the Turkish Government and the PKK focuses on a puzzling dynamic:
despite an aggressive military doctrine focused on engaging the PKK in a
conventional war resulting in the group declaring military defeat, the PKK not
only survived, but was able to regroup with remarkable speed. “By the early
2000s, the PKK was militarily weak, and the majority of its members were
outside Turkey’s borders. However, after a few years of calm, on 1 June 2004,
the PKK put an end to its unilateral ceasefire and once again began to attack
civilian and military targets in Turkey. How was the PKK able to survive and
rebuild itself in such a short period of time?” (Pusane, 2015, p. 727). Thus,
in the early stages of the conflict, the Turkish government tried to defeat
the PKK by inflicting the maximum number of casualties possible, with little
concern for whom within the organization was being targeted. Translating this
policy into the realm of social networks, this doctrine roughly equates to a
strategy of random node removal.

The robustness of a network to various types of node removal depends largely
on its structure. A salient characteristic that can be observed in the PICAN
is its highly uneven degree distribution. As shown by the previous section,
despite the majority of nodes in the network being either isolates or low-
degree, a small number of nodes have very high degree. A scale free network is
a graph in which the nodes’ degree distribution follows a power law, where the
probability $p(k)$ that a node has degree k is a function of $k^{-\alpha}$,
such that $p(k)\propto k^{-\alpha}$, where $\alpha$ is a constant. Figure 12
shows the empirical distribution of node degrees, compared with a power law
distribution.

The empirical degree distribution appears to loosely follow a power law
distribution of $k^{-2.58}$. According to Broido and Clauset (2019), “a
network is deemed scale free if the fraction of nodes with degree k follows a
power-law distribution $k^{-\alpha}$, where $\alpha>1$”, making the PICAN
weakly scale-free. These types of networks have properties that are of direct
relevance to counterinsurgency tactics; in particular, “a compelling
characteristic of scale-free networks is that a significant fraction of nodes
can be removed randomly without the network losing connectivity” (Verma and
Zhang, 2020). This is because the vast majority of nodes have a low degree,
while only a small number of nodes are responsible for the overall
connectivity of the network. If nodes are removed at random, it is unlikely
that a high-degree node would be removed.

A counterinsurgency strategy based on inflicting the maximum amount of
casualties against the PKK is analogous to a strategy of random node removal.
This was the strategy pursued by the Turkish Armed Forces (TSK) in the 1990s,
and it succeeded in weakening the PKK militarily to the point that “the PKK
was defeated conclusively in 1999 and several scholars argued that the party
would never recover” (Plakoudas, 2018). However, despite the fact that the PKK
incurred heavy losses, these were mostly foot soldiers. The leadership
structure was left intact, allowing the organization to regroup after only
four years.

Following its military defeat in 2000 and re-emergence in 2004, the PKK grew
rapidly. Figure 13 utilizes image metadata to track this growth, showing the
evolution of the network over time. Timestamps from photographs are extracted,
and the earliest timestamp for each node is chosen thereby indicating an
individual’s first known appearance in the field. Of the images that were
sorted into clusters, 41% (6373) contained machine-readable timestamps. This
analysis should be taken as an approximation of the PKK’s regrowth rather than
a precise model thereof. Many images in the sample do not contain readable
timestamps, meaning that an individual may have been present in the PKK for
far longer than the length of time suggested by the metadata; For example, the
earliest available timestamp for Duran Kalkan is in 2014, despite his cluster
containing several photos that appear far older.

Figure 13: Preferential Attachment Over Time using EXIF Data

A network is constructed from metadata-containing images. Nodes are colored
according to the oldest timestamp in that node’s image cluster. The five most
central nodes are numbered, and correspond to leaders. Newer nodes tend to
cluster around older nodes.

The top 5 most central nodes (in terms of betweenness) in this restricted
sample are labeled, and once again correspond to the PKK leadership. Node 1 is
Duran Kalkan, who despite the light color of his node has been in leadership
roles within the PKK since the 1990s. The bulk of his neighbours appeared
between 2008 and 2014. Nodes 2 and 3 are Bahoz Erdal and Kadir Celik, two
senior figures in the military wing of the PKK, the HPG. They co-appear most
frequently with individuals who first appear in the early 2000s, though their
sustained presence in the field is indicated by co-appearances with much more
recent recruits as well. Node 4 is Ali Haydar Kaytan, a co-founder of the PKK
who largely served in non-combat roles. Interestingly, he appears almost
exclusively with fairly recent recruits who first appear from 2014 onwards.
Node 5 is Mehmet Gurhan, a military commander whose neighbours first appeared
in the early 2000s. Despite incurring heavy losses throughout the 1990s, the
PKK was able to regroup around a small number of core members.

Figure 13 shows some evidence of preferential attachment, whereby new nodes
attach preferentially to already well-connected nodes. As newer, lighter nodes
appear, they tend to attach to darker and more well-connected nodes. In other
words, as newer recruits begin to appear in photographs, they tend to appear
alongside older, more established members. Importantly, it seems that
different elements of the leadership were important hubs at different periods
in the PKK’s resurgence. This likely reflects the fact that “the PKK developed
an advanced organizational structure, which included both military units and
those elements responsible for recruitment activities, ideological training,
propaganda efforts in order to increase awareness about the Kurdish question,
and fundraising.” (Pusane, 2015, p. 728). Erdal, Celik, and Gurhan, members of
the military leadership, were important hubs in the immediate aftermath of the
PKK’s return to armed struggle in 2004. Political leaders, such as Kaytan,
Kalkan, and Karaylian, act as important hubs for more recent recruits,
particularly those appearing since 2014. The PKK’s ability to withstand
generalized attacks (i.e. random node removal), as well as its regrowth around
key members (preferential attachment) are characteristics associated with
scale free networks. Thus, the structure of the PICAN aligns closely with the
historical resilience of the PKK itself.

###  7.2 Targeted Strikes

Assuming the topology of a co-appearance network generated from militant
photographs loosely approximates the general structural characteristics of the
group itself, the former can be used to make general inferences about the
future success of more recent counterinsurgency strategies.

Seemingly in recognition of the failure of its previous counterinsurgency
strategy, the Turkish government has embarked on a radically different
approach in recent years. Particularly since 2016, Turkish forces have focused
on targeting the leadership structure of the PKK through the use of airstrikes
and raids both within and beyond Turkey’s borders (Tastekin, 2016). In 2016,
Turkish forces erroneously claimed to have assassinated Bahoz Erdal, the
current leader of the PKK’s armed wing. The claim was spread largely by pro-
government media, and indicated that he had survived at least two prior
assassination attempts (Sabah, 2020). Rather than attempting to inflict the
largest possible number of casualties against the PKK by engaging in a
conventional ground war, Turkish forces appear to be focusing on targeting key
individuals within the organization.

In theory, a counterinsurgency strategy predicated on targeting the senior
leadership of the PKK should be more disruptive to the network than one based
on the removal of nodes at random; given the scale-free nature of the network,
“disabling just a few critical nodes can result in a disconnected network
especially for the smaller nodes” (Verma and Zhang, 2020). An empirical
assessment of this claim in reference to the PKK fills an important gap in the
applied literature on the social network analysis of insurgent groups: “claims
that scale-free militant networks are resilient due to their robustness
against random attacks on nodes have not adequately reckoned with the
countervailing effect that high-degree nodes are more visible” (Zech and
Gabbay, 2016, p. 233).

The selection of targets in this strategy is likely largely opportunistic;
previous operations have targeted PKK leaders on the basis of available
intelligence, which often results from chance encounters. In May 2021, the
Turkish President announced the assassination of Sofi Nurettin, a PKK military
officer responsible for the group’s presence in Syria (Sabah, 2021). The
operation was carried out by an intelligence unit formed specifically to
target Nurettin, which ultimately located him by tracking his family (Sabah,
2021). Nurettin survived an assassination attempt in 2016, suggesting that
Turkish forces had been targeting him for at least 5 years (Tastekin, 2016).
Beyond entailing a significant investment in terms of military and
intelligence resources, the operation’s success hinged significantly on luck.
Thus, the Turkish government’s new military strategy involves targeting PKK
leadership, but only managing to strike opportunistically.

The distributions shown in Figure 14 approximate the effects of such a
strategy on the LCC by randomly removing a given number of nodes T from the
set of 30 most central nodes. Each level of T roughly corresponds to the
aggressivity of a counterinsurgency strategy; the removal of only 3 random
nodes out of the top 30 most central nodes would correspond to a relatively
weak counterinsurgency approach, while the removal of 27 out of 30 corresponds
to an extremely aggressive strategy. In practice, the value of T is likely to
be determined by available opportunities. 1000 random sets of nodes are
removed at each level, and the resulting kernel density estimates of the
effect on LCC size are reported.

Figure 14: PICAN Robustness to Opportunistic Central Node Removal

Distributions reflect the effect of removing a random set of nodes from the
top 30 most central nodes on the relative size of the LCC. Colors indicate the
number of nodes removed, a procedure which is repeated 1000 times. High levels
of connectivity are maintained even when the majority of the top 30 nodes are
removed.

If only three out of the top 30 most central nodes are removed (the scenario
indicated by the distribution in blue), there is virtually no effect on
network connectivity. As more nodes are removed, connectivity decreases but
the variance of the distributions increases substantially. Thus, the extent to
which the removal of, for example, 15 nodes decreases the size of the LCC
depends heavily on which 15 nodes are removed. Bimodality in some of these
distributions likely indicates splintering brought about by either the removal
or survival of the three most central nodes. When 27 of the top 30 most
central nodes are removed, there is a strong likelihood of the relative LCC
size dropping to near zero. However, the smaller second peak on the right of
the distribution indicates that the LCC can retain roughly 20%– and in rare
cases over 40%– of its original size, depending on which three nodes out of
the top 30 survive. Similarly, though the removal of only 3 of the top 30
nodes overwhelmingly results in the LCC remaining unchanged, a much smaller
peak to the left of the distribution indicates potential reductions in LCC
size of up to 20%, presumably in the unlikely event that the three most
central nodes are removed.

Figure 15: Comparison between PICAN and a Random Graph

Compared to a random graph with the same number of nodes and edges, the PICAN
features a number of high-degree hub nodes (shown in red). There also appears
to be a higher degree of clustering and interconnectivity in the PICAN.

Though the theoretical literature on scale-free networks suggests that these
are highly vulnerable to the removal of hub nodes, the PICAN appears to be
surprisingly robust to such a strategy. Even when a random set of 15 of the
top 30 most central nodes is removed, the network tends to maintain well over
60% of its connectivity. Thus, while the top three most central nodes in the
PICAN stand out in terms of their centrality, a significant amount of the
network’s connectivity is distributed across a larger set of important
figures. Indeed, the FARC– a similarly hierarchical paramilitary organization–
was able to withstand the assassination of 53 of its leaders and three members
of its Secretariat (Segura and Mechoulan, 2017).

This type of resilience is characteristic of “small world” networks. This
class of networks overlaps with (and shares many of the properties of) scale-
free networks; they are characterized by the presence of “hub” nodes, and are
thus generally robust to random node removal. Small world networks are further
characterized by a short average path length; compared to a random graph with
the same number of nodes and edges, the presence of well-connected hubs
enables shorter paths between sets of nodes. Figure 15 compares the Largest
Connected Component of the PICAN with an Erdős–Rényi random graph with the
same number of nodes and edges, the former displaying the aforementioned hub-
and-spoke configuration.

In the PICAN, the average shortest path between nodes is 4.9; the random graph
has an average shortest path length of 6.4. This is largely due to the
presence of high-degree hub nodes that are clearly visible in the PICAN but
absent in the random graph. The nodes are colored by degree, with the random
graph exhibiting a much more uniform degree distribution. Importantly, the
PICAN not only contains well-connected hub nodes, but general areas of high
connectivity. This type of interconnectivity is also typical of small world
networks, which exhibit a higher degree of clustering– the extent to which a
node’s neighbours are also interconnected. A quantitative measure of the
extent of clustering in a graph is given by the Watts and Strogatz (1998)
clustering coefficient, defined as:

${c}_{i}^{ws}=\frac{{2E}_{i}}{{k}_{i}({k}_{i}-1)}$

Where $E_{i}$ is the number of edges between neighbours of node i, and K is
the degree of the node. The more interconnected a node’s neighbours are, the
higher the clustering coefficient, and the more robust that network will be to
the removal of the node. The average clustering coefficient of the PICAN is
0.0574 compared to just 0.0014 for the random graph, meaning that the former
is more densely interconnected and therefore robust to the removal of even
high-degree nodes.

Humphries and Gurney (2008) propose a formal test for small-world-ness (3)
based on the premises that, compared to a random graph, (1) a small-world
network will have a shorter average path length L and (2) a higher degree of
clustering C:

${\lambda}_{g}=\frac{{L}_{g}}{{L}_{rand}}$ (1)

${\gamma}_{g}^{ws}=\frac{{C}_{g}^{ws}}{{C}_{rand}^{ws}}$ (2)

${S}^{ws}=\frac{{\gamma}_{g}^{ws}}{{\lambda}_{g}}$ (3)

If the above conditions are met, then S>1 and the network may be considered a
small world. The value of S for the PICAN is 2.69, suggesting that the
organizational structure of the PKK resembles that of a small world network.

In a study of jihadist insurgent groups, (Sageman, 2011, p. 140) notes that
“Unlike a hierarchical network that can be eliminated through decapitation of
its leadership, a small-world network resists fragmentation because of its
dense interconnectivity”. Jemaah Islamiyah, a Southeast Asian jihadist group,
was effectively incapacitated following the arrest of its senior leadership;
the group’s extremely hierarchical nature left local cells unable to function
without direct orders from above (Sageman, 2011, p. 141). Despite the presence
of a few key hub nodes in the PICAN, there also exists a large pool of highly
interconnected nodes representing the second and third tiers of the
organization’s leadership. This affords the PKK a high level of robustness to
the targeting of even its most central figures. Furthermore, the node removal
simulations carried out in Figure 14 are carried out on a static graph. As
shown in the analysis of the PKK’s regrowth shown in Figure 13, the network’s
evolution over time is characterized by preferential attachment to hub nodes.
Not only would the survival of even a small number of these nodes allow for
the regrowth of the group; “new hubs will take the role of the eliminated ones
and restore the network’s ability to function.” (Sageman, 2011, p. 140).

These results suggests that the robustness of a co-appearance network
generated solely from militant photographs to certain node removal scenarios
broadly aligns with the true network’s historical resilience to analogous
counterinsurgency strategies. The scale-free structure of the PICAN renders
random node removal strategies ineffective, as connectivity is maintained by
the survival of a relatively small number of high-degree nodes. This is
congruous with the historical record: “Considering the fact that the PKK has
developed such a complex system of networks and institutions, as well as
various sources of funding both at the domestic and international levels, it
was no surprise that Turkey’s ability in weakening the PKK militarily in the
late 1990s did not bring an end to the PKK insurgency” (Pusane, 2015, p. 730).
Assuming even a tenuous relationship between the PICAN and the actual
structure of the PKK, the image co-appearance network is able to provide
contextual information relevant to an assessment of the future success of the
counterinsurgency strategy currently being undertaken by the Turkish
government. As a small world network, the PICAN tends to survive the removal
of the majority of its most central nodes due to dense interconnectivity among
them. Indeed, the preceding paragraph suggests that the PICAN probably
underestimates the extent of this interconnectivity. The corresponding real-
world conclusion is that the degree of hierarchy within the PKK is not so
extreme as to enable the group to be incapacitated by the removal of a few key
leaders. Thus, even a strategy of targeted strikes is unlikely to be effective
against the PKK, especially when one considers the high cost and low success
rate of such efforts.

##  8 Conclusion

The analytical approach elaborated in this study makes three distinct
contributions to the literature on the application of Social Network Analysis
to the study of insurgent groups. Firstly, it leverages an abundant but
underutilized source of primary data generated by militants themselves. While
the main challenge in the study of dark networks is the lack of data thereon,
a defining trend in the evolution of modern insurgency has been the use of
online media, particularly online visual propaganda (Dauber, 2009). Social
network analysts have recognized the rich relational information contained in
image co-appearances as a valuable tool for understanding social structures
(Lewis et al., 2008; Berry, 2006; Golder, 2008). By automating the process of
generating an image co-appearance network through facial recognition and
unsupervised clustering, the present approach is able to harness the vast
quantities of unstructured image data generated by insurgent groups. The
distribution of this methodology as an open source python package further
facilitates its use by researchers of dark networks.

The ability to process large quantities of primary data enables a second
significant contribution to the existing literature, which relies almost
exclusively on secondary sources. Social networks generated through media
reports, legal filings, and public statements require a significant amount of
pre-existing knowledge about the group in question, and inherently omit all
but the highest-ranking figures. Though selection bias in insurgent
photographs is still a problem, a foot soldier is probably more likely to
appear in the background of a group photograph than to be named in a
newspaper. Image co-appearance networks are thus likely to be far more
complete, and can be created without additional knowledge about the group in
question.

Finally, the use of co-appearances imposes a level of consistency in the
ordering of the network. In previous studies of insurgent groups, ties between
nodes have encompassed everything from marriage to casual acquaintance (Gill
et al., 2014), from cohabitation to “weak contact” (Koschade, 2007). To be
sure, co-appearance in militant photographs encompasses a wide range of
possible social interactions, from friends sharing a meal to a commander
posing with her subordinates. If two people are co-present in a photograph,
the nature and social significance of the link between them is unknown.
However, if the same person repeatedly appears with others in a large number
of photographs, there is probably an overarching reason. An image co-
appearance network mirrors the social forces that govern when and where
pictures are taken. In the context of an insurgency where virtually all social
interaction is mediated by the group’s structure, an image co-appearance
network will generally reflect these ordering principles, including divisions
between military units, ranks, and political factions: foot soldiers will co-
appear because they’re in the same platoon; commanders officiating training
academy graduations will appear once with each graduand and frequently with
each other; leaders who have fallen out are unlikely to pose together. The
network will also reflect performative aspects of the group’s self-perception,
emphasizing actors and narratives that align with the image militants wish to
present to the world, the precise nature of which depends on the ideological
characteristics of the group in question.

Thus, the use of co-appearances in militant photographs enables detailed
insights into the social, political, and organizational forces that structure
insurgent groups. The three analytical sections in this study demonstrate the
types of analyses that are possible with a co-appearance network generated by
militant photographs, using a large quantity of obituary images taken by the
PKK.

Section 5 demonstrates that a qualitative analysis of the nature of an
individual’s co-appearances can yield information on functional and factional
divisions within a rebel group. In the PKK, military commanders have high
betweenness centrality due to frequent co-appearances with subordinates, who
are themselves generally poorly connected nodes. Political leaders spend less
time with the rank and file, and more time with each other, leading to higher
eigenvector centrality. The nature of co-appearances even captures the effects
of Cemil Bayik’s marginalization following a failed leadership struggle:
despite being a founding member of the PKK, he has relatively few co-
appearances and appears in no photographs with the top leaders of the
organization.

Section 6 demonstrates a consistent relationship between an individual’s rank
and their centrality in the co-appearance network. This is achieved by
matching over 100 nodes in the PICAN with individuals appearing on lists of
wanted persons maintained by the Turkish government, with the value of the
reward placed on them acting as a proxy for rank. Linear models indicate that
over half of the variation in the reward offered for an individual’s capture
can be explained purely by the number of times they appear with others in
photographs. Exponential Random Graph Models show strong positive associations
between reward value and the likelihood of edge formation, suggesting that
higher ranking individuals are more well-connected in the PICAN. In both sets
of models, results are robust to the exclusion of the top leadership from the
analysis.

Section 7 shows that the co-appearance network may even approximate the
general structural characteristics of the insurgent group. A close reading of
the topology of the PICAN in reference to qualitative work on the PKK’s
history finds agreement between the PKK’s resilience to generalized attacks
and the PICAN’s structural robustness to an analogous node removal strategy.
If the PICAN can be assumed to roughly approximate the structure of the PKK,
then the Turkish military’s current approach of targeted strikes against
leadership is also unlikely to be effective; a simulation of targeted node
removal shows that the network’s connectivity is maintained by a large and
densely interconnected pool of high ranking individuals waiting in the wings.

While insights from these three sections are specific to the nature of the
image dataset and the insurgent group in question, images gathered from other
sources or rebel groups would manifest their own idiosyncrasies and highlight
different social and organizational processes. For example, an egocentric
network of a single insurgent cell or unit could be constructed from an
individual’s posts on social media, with temporal and geographical nodal
attributes derived from metadata. Footage from the commission of war crimes–
disturbingly rife online– could be used to understand whether incidents are
occurring at random, or whether the same individuals systematically co-appear.
Images collected from multiple groups could reveal the existence and nature of
links between members of both organizations. Though dark groups are
exceptionally difficult to study, the extraction of relational information
from images in the form of co-appearances between individuals can yield
detailed information on their social structure.

## Appendix

Figure A1: Mosaic of Input Images scraped from online PKK obituaries

This image mosaic was constructed from roughly 20,000 publicly available
images posted by the PKK to their online obituaries website, hpgsehit.com. A
version of this figure with an interactive zoom functionality to view
individual pictures can be accessed here.

Figure A2: Sample Obituary

The screenshot above was taken from an entry on the PKK’s obituary website.
The obituary includes a high defitinition portrait in the top right, some
basic biographical details on the left, and a set of images of the fallen
militant.

Figure A3: Representations of Martyred PKK Leaders

The images above are samples from facial clusters containing representations
of two PKK leaders who are no longer in the field, but whose likeness is still
present in the photographs. On the right, the likeness of Mahzum Korkmaz is
depicted on lapel pins which appear in 21 photographs. He was the PKK’s first
military commander, and despite being killed in 1986, remains an important
figure in the PKK. The images on the left were taken from the cluster
generated for PKK founder Abdullah Öcalan, who was arrested and imprisoned in
1999. His likeness is depicted on flags and murals that appear in the
background of 107 photographs. Interestingly, despite not being in the field
for the past 23 years, his node in the PICAN is the most central. Both of
these phenomena reinforce the notion that there is a relationship between an
individual’s importance to the PKK and their embedding in the image co-
appearance network. However, for the sake of conceptual clarity in the
definition of edges in the PICAN, these nodes are removed from the network.

Figure A4: U.S. State Department Wanted Poster for PKK Leadership

This U.S. State Department wanted poster details rewards for information
leading to the capture of PKK leaders Murat Karayilan, Cemil Bayik, and Duran
Kalkan. All three individuals occupy highly central positions in the PKK Image
Co-Appearance Network; They are respectively the 2nd, 8th, and 3rd most
central nodes in the PICAN in terms of betweenness centrality.

Figure A5: Central University Research Ethics Committee Approval Letter

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