# RoleSeer: Understanding Informal Social Role Changes in MMORPGs via Visual
Analytics

Laixin Xie  School of Information Science and Technology, ShanghaiTech
UniversityShanghaiChina<EMAIL_ADDRESS>,  Ziming Wu  Interactive
Entertainment Group, Tencent Inc.ShenzhenChina<EMAIL_ADDRESS>,  Peng Xu
User Experience Center, NetEase Inc.HangzhouChina<EMAIL_ADDRESS>,  Wei Li
College of Creative Design, Shenzhen Technology UniversityShenzhenChina
<EMAIL_ADDRESS>,  Xiaojuan Ma  Department of Computer Science and
Engineering, The Hong Kong University of Science and TechnologyHong KongChina
<EMAIL_ADDRESS>and  Quan Li  School of Information Science and Technology,
ShanghaiTech UniversityShanghaiChina<EMAIL_ADDRESS>
(2022)

###### Abstract.

Massively multiplayer online role-playing games create virtual communities
that support heterogeneous “social roles” determined by gameplay interaction
behaviors under a specific social context. For all social roles, formal roles
are pre-defined, obvious, and explicitly ascribed to the people holding the
roles, whereas informal roles are not well-defined and unspoken. Identifying
the informal roles and understanding their subtle changes are critical to
designing sociability mechanisms. However, it is nontrivial to understand the
existence and evolution of such roles due to their loosely defined,
interconvertible, and dynamic characteristics. We propose a visual analytics
system, RoleSeer, to investigate informal roles from the perspectives of
behavioral interactions and depict their dynamic interconversions and
transitions. Two cases, experts’ feedback, and a user study suggest that
RoleSeer helps interpret the identified informal roles and explore the
patterns behind role changes. We see our approach’s potential in investigating
informal roles in a broader range of social games.

social role, social network, graph embedding, gameplay

††submissionid: 8648††journalyear: 2022††copyright: acmcopyright††conference:
CHI Conference on Human Factors in Computing Systems; April 29-May 5, 2022;
New Orleans, LA, USA††booktitle: CHI Conference on Human Factors in Computing
Systems (CHI ’22), April 29-May 5, 2022, New Orleans, LA, USA††price:
15.00††doi: 10.1145/3491102.3517712††isbn: 978-1-4503-9157-3/22/04††ccs:
Human-centered computing Human computer interaction (HCI)††ccs: Human-centered
computing Visualization††ccs: Human-centered computing User studies

##  1\. Introduction

Like in real-life communities, heterogeneous social dynamics are also observed
in virtual communities. Massively multiplayer online role-playing games
(MMORPGs) are essentially virtual communities in the form of role-playing. As
the gameplay takes place, members of the community adopt different social
roles (i.e., “a set of expectations and norms that define how people playing
the roles should and would behave in a social situation” (Ashforth, 2000;
Herrmann et al., 2004)). Some roles are public and official. They can be
easily observed from the given positions or titles, often labeled as warriors,
mages, hunters in games like World of Warcraft (WoW). As these roles are
evidently pronounced by the game mechanism and independent from the individual
behavior patterns, they are often recognized as formal roles (Ang and
Zaphiris, 2010). However, compared to the formal roles, some roles are less
evident but can be equally important. For instance, a member’s social status
is primarily influenced by its contribution to the community. When an old
member helps a newcomer, it adds to the effort of building a healthy community
culture – Altruism behaviors like such fortify the sense of belonging, which
will eventually benefit the community as an entirety. Alternatively, players
may also only take periphery positions and play as bystanders. The difference
of these acts may marginally affect the formal roles but subtly shift the
community’s social structure. Based on players’ respective influence upon the
social attributes, roles such as dungeon leader, fighter, isolate, social
butterflies, and information giver are naturally assigned based on their
explicit play behaviors (Ang and Zaphiris, 2010; Ashforth, 2000; Golder and
Donath, 2004; Herrmann et al., 2004). Following social studies (Ashforth,
2000; Herrmann et al., 2004), we use informal
roles111https://eagle.northwestu.edu/faculty/gary-gillespie/roles-and-small-
group-communication/ to represent the later, less evident roles, which are
naturally determined by players’ gameplay behaviors.

Characterizing the collective configuration of informal roles in a community
and understanding the existence and changes of roles within are essential ways
to study MMORPG communities. First, informal roles are greatly influenced by
existing formal roles. They will often replace or supplement formal social
roles (Smith, 1966), which can provide insights into the sociability design of
gameplay mechanisms that can facilitate the formation of specific informal
roles (Anderson*, 2004). Second, analyzing how individuals’ informal roles
vary regarding their behaviors can promote the long-term operations of a
virtual community, such as drawing in lapsed gamers and encouraging, e.g.,
“isolate” players to change their roles.

However, identifying informal roles and understanding the reasons behind their
existence and changes is nontrivial due to several issues: (1) Loose
definition. Existing literature has attempted to provide descriptions of
informal roles in MMORPGs. For example, an early study on text-based multi-
user dungeons (often referred to as the predecessor of MMORPGs) categorized
the typology of roles into four groups (Bartle, 1996): killers who like to
annoy other players; socializers who want causal social interaction with other
players; achievers who aim to master the game, and explorers who enjoy
exploring the game world. Although they provide grounded theories for later
research on social roles in the context of graphical MMORPGs, the current
definitions of the limited set of informal roles are primarily based on prior
knowledge of game designers and analysts. Consequently, existing methods for
deriving meaningful roles or patterns using pure statistical analysis
approaches are insufficient without a delineated definition of informal roles,
especially for those unspoken ones, making automated solutions of informal
roles mining challenging to achieve. (2) Dynamic interconversion and
evolution. Informal roles are based on human needs for trust, support,
resource sharing irrespective of whether they operate in real life or a
virtual setting (Lai and Turban, 2008). Specifically, these roles result from
social interaction and negotiation between the actor and those they interact
with. They are dynamically developing and interconvertible over time, i.e.,
players may switch from one role to another. Given such characteristics of
informal roles, summarizing and understanding the temporal properties of the
dynamic interconversion and evolution of informal roles in an MMORPG virtual
community can help reveal players’ exploration patterns and shed light upon
the effective socialization mechanism of the players from a global
perspective. (3) Diverse paths behind role formations. Diverse steps of social
exploration (i.e., social self-discovery helping an individual understand
their own social needs (Jenkins, 2000)) precede forming a particular informal
role. For example, players may need to balance their personal and social time
or choose between large but weak social ties and a few strong social ties when
interacting with other virtual companions in the game (Moreland and Levine,
1982; Morrison, 2002). Although a small number of players with abundant
external resources (e.g., time and money) could take on specific informal
roles through the spending of these resources, the large majority of the
members in the game community may not have sufficient external resources at
their disposal. Instead, players adopt some informal roles through gameplay
interactions. Inspecting the diverse paths behind the formations of such
informal roles is more important because they can explain how people interact,
collaborate, and work together to cultivate community building and growth. It
can also increase the participant’s awareness of their social interaction
(Strijbos et al., 2004).

Most previous work tends to study the social role from “who the users are” and
then “what the users do” rather than behavioral interactions between them in
the virtual community, or they assumed a prior knowledge about the social
roles (e.g., focusing on the contents of the players’ posting or activities)
(Ang and Zaphiris, 2010; Canossa et al., 2019; Williams et al., 2014).
Although such studies have been helpful, we argue that focusing on behavioral
interactions would cast new light on social roles in virtual worlds. Thus, to
fill the gap, we propose RoleSeer, an interactive visual analytics system that
helps game designers and game user experience (UX) practitioners understand
informal roles and paths behind their formations and dynamic changes in the
context of an MMORPG. To give detailed elaborations on the proposed idea and
follow-up implementations, we organize this work as follows. We first observe
our collaborating game team’s current gaming social role analysis practices
and identify their primary needs and concerns. Then, we adapt a dynamic
network embedding and alignment approach to the friendship network in the
specified MMORPG for facilitating potential informal role detection across
multiple time snapshots. Different clusters of potential informal roles and
their interconversion and evolution across these clusters are discovered by
projecting the resulting embeddings onto a low-dimensional space. We further
support the experts to explore players’ diverse behavioral interactions that
lead to their role changes. Based on these objectives, we develop a visual
analytics system to support fine-grained informal role analysis at the
overview, role cluster, and individual levels. Lastly, we present several case
studies and interview feedback with domain experts to evaluate the efficacy of
our system. We outline the contributions of this work as follows.

  * •

We shadow domain experts’ daily working processes and conduct interviews to
get insight into their current practice in understanding informal role
changes.

  * •

We identify the potential informal roles from the perspective of behavioral
interaction analysis through an adapted dynamic network embedding and
alignment model in the context of an MMORPG.

  * •

We depict the interconversion and evolution of informal roles across different
time snapshots and explore the patterns behind the role changes via a visual
analytics system.

##  2\. Related Work

Literature that overlaps this work can be categorized into three groups:
social network analysis in MMORPGs, graph latent representations, and dynamic
graph visualization.

###  2.1. Social Network Analysis in MMORPGs

Studies that focus on social network analysis in MMORPGs suggest that players’
social behaviors and interactions considerably influence players’ gaming
experience (Ducheneaut et al., 2006a; Jia et al., 2015; Kirman et al., 2009;
Wohn et al., 2010). For example, Szell and Thurner (Szell and Thurner, 2010)
studied the structure of friend, enemy, and communication networks and
identified that friend and enemy networks are topologically different.
Ducheneaut et al. (Ducheneaut et al., 2007) and Chen et al. (Chen et al.,
2008) used traditional metrics in social network analysis such as density and
centrality to analyze the properties of player guilds in WoW. Lu et al. (Lu et
al., 2019) proposed BeXplorer to explore the dynamic interplay among multiple
types of behaviors. Li et al. (Li et al., 2017) investigated the evolution of
egocentric players and focused on the relationship between a player (ego) and
his/her directly linked friends (alters). They also inferred how changes in an
ego’s interactive behaviors might propagate through the friendship network.
However, their work captured the evolutionary pattern based on ego networks at
a microscopic level, a case-by-case analysis. Our work identifies
representative informal roles from the perspectives of structural analysis at
a global level. Furthermore, we study the underlying patterns that drive the
interconversion and transition of informal roles held by players across
multiple time snapshots.

Apart from studying the overall social relationships within a virtual
community, other studies attempted to investigate players’ social status or
social roles such as leader, core members, and newcomers (Ang and Zaphiris,
2010). For example, Williams et al. (Williams et al., 2014) conducted
interviews with hardcore players and demonstrated the importance of leaders
and critical structural positions within a virtual community. Canossa et al.
(Canossa et al., 2019) applied standard social network features to identify
“influencers” to an online multiplayer shooter game. The result shows that
network feature-defined influencers had an outsized impact on the playtime and
social play of players joining their in-game network. These studies shed light
upon the social role analysis in the game community. However, the previous
social roles are pre-defined in an initial set of categories such as
“influencer” and “hardcore player” based on experts’ prior knowledge or
manually categorized using thematic and content analysis (Ang and Zaphiris,
2010), which cannot identify more unspoken informal roles. Ang et al. (Ang and
Zaphiris, 2010) observed the “chat interaction” to determine the structural
characteristics of three social roles of a guild community in WoW: densely
connected core members, loosely connected semi-periphery members, and
disconnected periphery players. However, they only focused on a small guild in
one static snapshot and verbal interactions among players. In our work, we
identify informal roles based on behavioral interactions. We study the
players’ non-verbal interaction and explain the interconversion and
transitions of informal roles. Ducheneaut et al. (Ducheneaut et al., 2006b)
analyzed the prevalence and extent of social activities to investigate whether
(and how) a game’s “social factor” can contribute to the game’s success. They
found that playing the game is like being “alone together,” i.e., surrounded
by others but not necessarily actively interacting with them. Their study
suggests designs for online games in which encouraging and supporting direct
interactions might be “less important” than designing for the “spectator
experience” and a sense of social presence, indicating different social
interactions and roles may be equally important to the game’s success.
Motivated by their work, we study informal roles’ existence and dynamic
transitions for a better user experience.

###  2.2. Graph Latent Representations

Graph latent representations have demonstrated their practicality in many
graph analysis applications and downstream machine learning tasks such as node
classification, clustering, and link prediction (Cai et al., 2018). Perozzi et
al. (Perozzi et al., 2014) developed a random walk-based method, DeepWalk, to
learn latent representations for nodes by generalizing neural language models
to preserve the higher-order proximity between nodes. Node2Vec (Grover and
Leskovec, 2016) provides a trade-off between breadth-first search (BFS) and
depth-first search (DFS) in the random walk process. Van den Elzen et al. (van
den Elzen et al., 2015) derived snapshot representations from adjacency
matrices and then projected the snapshots onto a 2D space for network state
discovery. Dal Col et al. (Dal Col et al., 2017) used the coefficients of
graph wavelets to represent nodes for evolutionary local change discovery. Xu
et al. (Xu et al., 2018) exploited diachronic node embeddings (DNE) for better
preserving structural and temporal properties. They further designed a system
for an interactive exploration of dynamic networks. Particularly, its backend
model first extracts node embeddings of different timestamps and then applies
embedding alignment, which shows strong capability in capturing local
information, i.e., the similarity among players. Eren et al. (Cakmak et al.,
2020) displayed the similarity and difference of temporally dynamic graphs by
sorting the graph embedding vectors. Our approach is different from the above
works on two dimensions. First, they applied DeepWalk to the network at each
time snapshot and initialized the embedding results to preserve the temporal
community, while we replaced the underlying DeepWalk with Struc2Vec (Ribeiro
et al., 2017) for baseline model comparison. The adapted version can maintain
the structural similarity that captures the “roles” of nodes in the graph.
Second, we focus on the interaction patterns that explain informal roles’
existence, interconversion, and transitions.

###  2.3. Dynamic Graph Visualization

Dynamic graph visualization has been attracting researchers’ attention for a
long time. Two primary visualization methods have been proposed: animation and
timeline-based methods. Particularly, the former method simulates the graph
dynamic evolution by redrawing the graph at each step, visualizing it with a
node-link diagram (Beck et al., 2017), showing its structural characteristics.
However, animation techniques are desirable to reduce the complexity of
dynamic graphs to facilitate visualization; however, they are inadequate to
support detailed network analysis and interpretation of temporal properties
(Archambault et al., 2010). Meanwhile, users must maintain a mental map in
each snapshot for comparison (Archambault et al., 2010; Bach et al., 2013).
Instead of animating the network as a sequence, timeline-based techniques
directly draw the network in a static image at each time step along a
timeline, providing a better temporal overview of the dynamic graph (Groh et
al., 2009). Small multiples such as matrices (Perer and Sun, 2012) and node-
link diagrams (Beck et al., 2017) are employed to study position changes of
nodes within the dynamic graph (van den Elzen et al., 2013). Although these
methods can capture temporal structural changes, human cognition often limits
the discovery of temporal patterns. Furthermore, when analyzing an extensive
network, one may encounter scalability difficulties when space is limited
(Federico et al., 2012; Pohl et al., 2008). In this work, we combine timeline
visualization with our graph latent representations. Precisely, the latent
graph representation captures the structural similarity and dynamics within a
short period (e.g., $6$ hours for one step). The timeline aligns the
corresponding informal roles with visualizing the overall trend across the
entire time steps. Thus, we could accurately capture the dynamics since we
analyze the graph in a much shorter period while still maintaining an overview
of the overall changes, achieving a balance of detail and abstraction.

##  3\. Observational Study

This section presents the background information of the showcased game and the
game team we collaborate with. Then, we shadow the experts’ daily working
process and summarize their conventional practice and bottlenecks. Finally, we
distill the design requirements based on the results of expert interviews.

Table 1. Event and intimacy: $X$ indicates varying intimacy values of different events. $N$ shows no upper bound. | killing monster | killing player | task | Using props | fighting | chatting | carbon | battle
---|---|---|---|---|---|---|---|---
each increase | 1 | 1 | X | X | 25 | 2 | X | 120
weekly bound | 1500 | 300 | 100 | 5000 | 350 | 210 | N | N

###  3.1. About the Game and the Team

The game studied in this work is a typical MMORPG that provides various
systems to facilitate social interactions among players, such as text/voice
chatting and in-game activities. Among all the systems, the friendship system
is the most popular one, allowing players to finish specific tasks, e.g.,
adding others as friends, seeking help, or finding teammates for challenging
dungeons. Typically, friendship closeness is measured by the “intimacy value”
between two players, designed by the game team to evaluate the degree of
social interaction between two players. Different interactions such as
chatting, killing monsters, and battles among players contribute to varying
intimacy values (Table 1).

Figure 1. The impact of the number of players’ social relations established in
the first week on the players’ retention rate in the next week: x-axis
indicates the number of social ties found in the first week. The top half
y-axis shows the retention rate of players in the next week, and the bottom
half y-axis is the number of the involved players. Three red rectangles
indicate the retention differences between players with $0$ and $1$ social
relation in terms of three intimacy thresholds (i.e., different values of
intimacy).

We collaborated with a team of experts from an internet game company,
including one user experience (UX) engineer (E1, female, age: $28$), one data
analyst (E2, male, age: $29$), and two game designers (E3, male, age: $29$,
E4, male, age: $30$), to study players’ socialization in the MMORPG as
mentioned above. All experts have been in the game industry for more than five
years, and they have been involved in this specific MMORPG since its
preliminary inception. One of the primary responsibilities of the game team is
constantly improving the game’s retention rate. According to a preliminary
analysis of the game team (Figure 1), players with certain social relations
have a 20% higher retention rate than those without social ties (indicated by
the three red rectangles). Meanwhile, specific turning points can be
witnessed. For example, players with $5$ – $7$ social relations have a
considerably higher retention rate than those with fewer social ties. The
experts commented that social ties could help improve players’ retention, thus
considering $5$ – $7$ social relations the target value of players’
socialization guidance in the early gaming stage. This initial observation
also motivates our work. The game team wanted to observe how players establish
social relations and get involved in the community, so they could design
better social mechanisms to support players’ socialization.

###  3.2. Experts’ Conventional Practice and Bottlenecks

To obtain detailed information of the game experts’ current practice, with
consent, we shadowed the team’s daily working process, including videotaping
how they observed players experiencing the game, conducting testing
experiments, and on-site interviews with the players. Later, we carried out a
retrospective analysis with the game team on their conventional practices.
Particularly, to tackle the issue mentioned above, the game team attempted to
use a node-link diagram to visualize how players establish their social
relations over time. They found that at the beginning of the game, three or
four players may set up a small group, and the connections within the group
become close. Then, the small group begins to establish social contact with
external players, followed by integration with another group. Finally, the
newly formed group integrates into a larger one. The experts identified that
most players (approximately 60%) follow a similar procedure in-game
socialization, and their topological positions in the entire social network
are quite different. For example, in Figure 2, red dots indicate players
entering the game after the first day of game release (marked as PAs), and
black dots indicate players entering the game on the first day (marked as
PBs). E1 compared the top three largest communities and drew the following
three conclusions. First, in the first network (Figure 2 (a)), the proportion
of PAs is higher compared with that in the other two networks (Figure 2 (bc)).
Second, most PAs are in the central areas of the network (Figure 2 (a)).
Third, PAs establish more social relations in Figure 2 (a), i.e., red dots
with only one social tie are few. Many nodes have been integrated into groups
or have established multiple social relations. In Figure 2 (bc), PAs are
mainly distributed in the peripheral areas with few social relations.

Figure 2. The force-directed layout of the social networks in three virtual
communities. (a) PAs are well integrated into the community. (b – c) PAs are
mainly distributed in the peripheral areas and have fewer social relations.
Figure 3. Pipeline of RoleSeer: (1) data processing to produce dynamic graphs;
(2) back-end engine consists of a graph embedding model and a clustering
module to identify informal roles, and (3) front-end visualization facilitates
explorative data analysis.

Although the experts obtained some initial insights into how players’
socialization affects the integration into the community, they encountered the
following issues when trying to make sense of players’ exact social positions
and the underlying reasons that lead to their social positions. First, the
game team distinguished players based on “who the players are” and “what the
players do” and compared their social positions from an overview perspective.
The experts felt it insufficient to analyze players’ structural similarities
and differences. Second, from the obtained node-link visualizations, the
experts could observe the changes of players’ social positions, e.g., “some
players are moving from the peripheral positions to more central areas while
others remain unchanged” (E1). Nevertheless, given the complexity and dynamics
of the network, they have no idea how to track the movement of players’ role
changes in detail. Third, the current practice enabled the experts to observe
the retention rates of different players (e.g., players with a different
number of social relations). However, they had no idea what the collective
interactions are that lead to such role differences, “if we know what they
are, we could design more appropriate social mechanisms,” said E3.

###  3.3. Experts’ Needs and Expectations

We interviewed E1 – E2 in one session and E3 – E4 in another session to
identify their primary concerns about analyzing players’ social status and
actions that contribute to social roles. Each session for the participants
lasted about half an hour. At the end of the interviews, the need for a visual
analytics system to ground the team’s conversation with social role analysis
emerged as a key theme. We summarize their requirements as follows.

R1 Distinguish potential informal roles. According to the experts’ feedback,
defining informal social roles largely depends on the experts’ prior
knowledge, assuming that some typical informal roles can be quickly formalized
from the perspective of “conventional” social science. For instance, the
experts would design rules based on what they did to identify the players.
“Although the designed rules can classify players holding different roles, the
approach can be expensive and cumbersome, and uncertainty exists whether more
effective rules could be extracted to cover more social roles,” said E2.
Therefore, they wished for a more effective way to distinguish potential
informal roles.

R2 Demonstrate information of corresponding informal roles. After potential
informal roles are identified, a subsequent analytical task raised by the
experts is to interpret these roles. For instance, E1 pointed out that “we
need to determine how the role members are positioned and interact with their
neighbors.” E2 commented that “we should be able to observe the similarities
and differences among the detected informal roles.” Specifically, each
informal role should be inspected to allow the experts to take a closer look
at their local social structure information and characteristics.

R3 Display interconversion and transition of informal roles. Uncovering
dynamic interconversion and transition among informal roles can facilitate
summarizing the temporal patterns in the virtual community within a particular
period. For example, E1 wanted to identify the evolving trajectories among the
informal roles, which can demonstrate the role changes of players and the
accompanying statistical information.

R4 Track the evolution of informal roles over time. All experts mentioned that
investigating the global picture of how a collection of informal roles evolves
across multiple timestamps is critical to understanding the overall patterns
of role changes in the virtual community. For example, E3 commented that “we
can know which role occupies the majority of the game network over time” and
“how the minority gradually evolves into the majority,” said E4. Therefore, an
intuitive visualization is demanded to achieve such analytical purposes.

R5 Summarize interactions leading to informal role changes. E3 – E4 indicated
that understanding the paths behind such informal role changes is critical for
designing appropriate social mechanisms and guidelines. Given various
interactions that can determine a player’s socialization and engagement that
result in a player’s informal role, our approach should provide a clear role
change trajectory and details of actions at both cluster and individual
levels.

##  4\. Overview of RoleSeer

Based on the requirements mentioned above, we propose a visual analytics
system named RoleSeer to support domain experts to better inspect informal
roles and patterns behind their dynamics in the context of a given MMORPG.
Figure 3 demonstrates the system architecture and pipeline. First, RoleSeer
processes in-game interaction logs from the MMORPG and constructs undirected
graphs to denote players’ interactions and relationships from raw data. These
undirected graphs are then fed into the back-end engine, which uses the
proposed graph embedding model and clustering module to capture structural
identity and infer informal roles (i.e., player cluster) (R1). To investigate
and understand the potential informal roles, multi-level visualizations are
integrated into RoleSeer to fulfill the requirements from a coarse level to a
fine-grained level. The front-end visualization consists of a Network
Overview, a Role Transition Overview, a Role Transition Projection, an Event
Interaction View, and an Individual Interaction View. The network overview
enables domain experts to look through all snapshots and their metric
statistic information, and the experts can quickly locate the snapshot of
interest (R2). The role transition overview focuses on the interconversion and
evolution of informal roles. The experts can observe the characteristics of
each informal role and the transitions between a variety of informal roles in
a comparative manner. In a specific informal role, the experts can figure out
which cluster the players within the cluster come and go (R3, R4). The role
transition projection demonstrates the metric changes of the corresponding
players that experience role transitions. Finally, the event interaction view
and individual interaction view jointly provide evidence from the interaction
event sequence during role transition. Thus, the experts could infer the
potential reasons behind the role transition and obtain rich insights (R5).

##  5\. Back-end Engine

The back-end engine first constructs a dynamic interaction network that models
social interactions among players in the showcased MMORPG. Then, it generates
the network embedding representation that captures the structural identity
throughout players’ dynamic gaming process. The resulting network embeddings
are reduced to a low-dimensional space for node clustering to facilitate the
detection and exploration of the potential informal roles.

###  5.1. Dynamic Social Network Construction

The players’ dynamic interactions throughout the gameplay are depicted by a
dynamic social network. Specifically, we model the dynamic social network as a
sequence of $T$ snapshots {$G_{1},G_{2},...,G_{T}$}. $T$ denotes the number of
time steps. Each snapshot is an undirected graph $G_{t}$ = $(V_{t},E_{t},t)$,
where $V_{t}$ represents the set of the existing or active players at the
$t^{th}$ snapshot. The edges $E_{t}$ denotes social relation strength measured
by the latest intimacy value between the players at $t$. The intimacy value
between two players is accumulated when an interaction between them happens.
As suggested by the game team that the average gameplay duration for an in-
depth player in this MMORPG is around $6$ hours, we set the size of the
associated time window to $6$h for each snapshot. To reduce the computation
time for constructing the network, we only reserve active players within each
snapshot, i.e., if a player is temporarily offline within $6$h, he/she would
be excluded in the network because the player does not contribute to the
network evolution. When he/she returns to the game, his/her interactions with
the others and the corresponding intimacy would be restored.

###  5.2. Proposed Method and Baseline Models

To infer potential informal roles, we need a latent representation of each
node (i.e., each player) to capture its structural identity in the dynamic
evolving network. We introduce three representative dynamic network embedding
methods: 1) Diachronic Node Embedding (DNE) (Xu et al., 2018), 2) an alignment
version based on struc2vec (Ribeiro et al., 2017), i.e., $struc2vec_{align}$,
and 3) dynAERNN (Goyal et al., 2020). DNE learns node embedding by modeling
neighboring local information in a dynamic graph. However, it is incapable of
learning structural identity due to the nature of deepwalk used in DNE, i.e.,
the structurally similar nodes that locate far apart will not have equal
representation according to deepwalk (Perozzi et al., 2014). To eliminate the
limitation, we replace deepwalk with struc2vec, which encodes node similarity
at different scales and thus can have a global perspective. The proposed
method is named $struc2vec_{align}$. Similar to DNE, we attach a TransE
(Bordes et al., 2013) module to struc2vec, which aims to align the struc2vec
embeddings between the continuous networks. On the other hand, the state-of-
art dynAERNN proposes an encoder-decoder architecture to predict missing links
in a dynamic graph, and the last hidden layer outputs the learned embeddings.
After the embeddings are generated, we use the dimensionality reduction
technique t-SNE to project the node embeddings generated from the above three
approaches into a $2$-dimensional space. Then, we cluster the projections
using X-Means (Pelleg and Moore, 2000), an optimized clustering algorithm that
extends K-Means in terms of calculation efficiency and efficient estimation of
the number of clusters. The resulting clusters are then considered as informal
role candidates. In the following subsection, we first design evaluation
metrics to compare and evaluate the node embeddings generated from the above
three approaches and then explore the identified informal roles.

###  5.3. Evaluation Metrics

The purpose of node embedding and subsequent clustering is to identify the
potential informal roles a player may experience over time. Thus, an informal
role cluster should capture diverse temporal information, maintain the
structural identity within a cluster, and maximize the inter-cluster
difference. In other words, our evaluation needs to examine which intra-
cluster node embedding method can better maximize the diversity of temporal
information and maintain the structural identity (i.e., minimize the
structural difference). We hence adopt the following local and global metrics
(Li et al., 2018), including local metrics like 1) Degree; 2) Leverage
Centrality that depicts neighboring relationships, and global metrics 3) Page
Rank; 4) Closeness; 5) Within Module Degree, and 6) Betweenness. In addition,
to capture the diversity of temporal information, we use 7) Time Distribution
as a statistical metric to measure the intra-cluster temporal diversity. To
calculate the intra- and inter-cluster differences in terms of the above
metrics, we adopt an Inter-Cluster Metric as

(1) $\displaystyle\frac{1}{\|S\|}\sum_{\begin{subarray}{c}(p,q)\in S,\\\ p\neq
q\end{subarray}}KL\\_Divergence(hist(Met_{i}(p)),hist(Met_{i}(q)))$

to compute the mean of KL Divergence, where $S$ is the set of the clusters,
$hist(\cdot)$ denotes the histogram of the normalized $i^{th}$ metric.
$Met_{i}(\cdot)$ outputs a vector containing the $i^{th}$ metric of all nodes.
We use an Intra-Cluster Metric as

(2) $\frac{1}{\|S\|}\sum_{p\in S}var(Met_{i}(p))$

to measure the mean of variances of metrics within one cluster, where $var(x)$
represents the variance of $x$.

Table 2. Inter-cluster metrics for different models (the larger, the better) Model | Degree | PageRank | Betweenness | Leverge Centrality | Within Module Degree | Closeness
---|---|---|---|---|---|---
$DNE$ | 0.821670504 | 0.411103418 | 0.140934534 | 0.16379114 | 0.780515731 | 1.369339797
$dynAERNN$ | 0.796215423 | 0.398164498 | 0.459215495 | 0.377308901 | 0.32386315 | 0.712463516
$struc2vec_{align}$ | 3.366409078 | 1.298493177 | 0.940996241 | 1.314054328 | 0.586676757 | 1.681372761
Table 3. Intra-cluster metrics for different models (the smaller, the better) Model | Degree | PageRank | Betweenness | Leverge Centrality | Within Module Degree | Closenes
---|---|---|---|---|---|---
$DNE$ | 0.004741896 | 0.001819743 | 0.00066172 | 0.026901872 | 0.004532516 | 0.012460878
$dynAERNN$ | 0.00917859 | 0.001512473 | 0.000947724 | 0.022042176 | 0.008951476 | 0.01330977
$struc2vec_{align}$ | 0.007649969 | 0.002744551 | 0.002403099 | 0.021968981 | 0.010843563 | 0.012954739

###  5.4. Result Analysis

It is infeasible to evaluate the entire set of players due to the high
computation complexity. Therefore, we thereby randomly sample a set of players
within $3$-time snapshots (i.e., $18$ hours in total). We then unify all the
generated network embeddings from the three methods as $128$-dimensional
vectors to calculate the abovementioned metrics. Figure 4 shows the fine-
grained box-plot statistics of time distribution from the three methods. The
time distribution within each cluster is very concentrated, i.e., only
comprising players from one time snapshot, implying that dynAERNN does not
maintain the diversity of temporal information well. On the contrary, the
distribution in the other two methods, i.e., DNE and $struc2vec_{align}$, are
more diverse within one cluster, particularly the result in Figure 4 (c)
achieves the best differentiation in terms of time distribution. To compare
the metric performance more quantitatively, we demonstrate the inter-cluster
and intra-cluster metric results in Table 2 and Table 3, respectively. It is
observed that most inter-cluster metrics of $struc2vec_{align}$ are higher
than those in the other two methods, especially the two local metrics, i.e.,
Degree ($3.366409078$) and Leverage Centrality ($1.314054328$). In the intra-
cluster metrics, the difference among the three models is relatively trivial
compared to the gaps in Table 2. Although DNE wins in most metrics,
$struc2vec_{align}$ still shows a comparable performance. Furthermore, the
performances in both the inter- and intra-cluster metrics are similar to the
conclusion attained by EmbeddingVis (Li et al., 2018), which confirms that
struc2vec-based embeddings preserve Degree better than the deepwalk-based
methods. To sum up, the experiments demonstrate that our proposed
$struc2vec_{align}$ can well maintain structural identity and capture diverse
temporal information over the dynamic network. Therefore, we use the network
embeddings generated by $struc2vec_{align}$ to identify the potential informal
roles.

Figure 4. Time distribution over clusters, where the orange line denotes the
median. The time distribution within each cluster in dynAERNN is very
concentrated, implying that dynAERNN does not maintain the diversity of
temporal information well. On the contrary, the distribution in the other two
methods, are more diverse within one cluster, particularly,
$struc2vec_{align}$ achieves the best differentiation in terms of time
distribution.

##  6\. Front-end Visualization

RoleSeer augments familiar visual metaphors to enable domain experts to focus
on analysis. We strictly follow the mantra “overview first, zoom and filter,
then details-on-demand” (Shneiderman, 2003) to guide users in exploring the
formation, interconversion, and transition of informal roles in the context of
an MMORPG. Based on these principles and the preceding requirements, we
develop four visualizations that allow the output of the back-end model to be
quickly inspected at the overview, role cluster, and individual levels.
Specifically, we design a network overview (Figure 5(a)) to locate the
snapshot of interest and observe its characteristics in terms of various graph
metrics; a role transition overview (Figure 5(b)) and a projection view
(Figure 5(c)) to assist domain experts in obtaining an overview of the
characteristics of the potential informal roles and the transitions among
them; an event interaction view to explore the reasons behind role changes,
and an individual interaction view to visualize individual’s interactions with
other counterparts (Figure 5(d)).

Figure 5. System interface of RoleSeer. (a) The network overview supports data
selection and summarizes the metric distribution across multiple snapshots.
The metric statistics comprise four in-game metrics (left) and six graph
metrics (right). (b) The role transition overview (i.e., unexpanded snapshot)
shows the overall interconversion and evolution across snapshots (f), and the
expanded snapshot presents the role transition within a snapshot (e). (c) The
role transition projection projects the graph embedding results of players
within selected informal role clusters. The players with transition are
highlighted by grey lines and linked across two informal roles. Metric changes
are also visualized in the middle axis between the two projections. The
selected player’s metric changes are highlighted (g). (d) The event and
individual interaction view shows the projection results of players’ event
patterns and narrates the selected ego’s interaction with other alters, as
well as the percentage of each event.

###  6.1. Role Transition Overview

The role transition overview is designed for three purposes. First,
appropriate metrics should be selected to represent each detected informal
role cluster (P1). The clusters are generated by the X-Means algorithm that
accepts the output of the embedding vectors generated by the
$struc2vec_{align}$ mentioned above. However, interpreting these “clusters”
from the perspective of the embedding vector is rather abstract because the
dimensions in a typical embedding do not have a particular meaning (Smilkov et
al., 2016). Instead, the experts are more familiar with specific metrics such
as PageRank and betweenness. Therefore, showing appropriate metrics
representing each identified informal role cluster can help domain experts
characterize and understand the role clusters. Second, the difference between
the role clusters should be presented to facilitate the exploration and
understanding of the cluster. Visualizing the characteristics of each role
cluster is necessary for evaluating that the entities in the cluster are
collectively gathered in a meaningful manner. Precisely, metric distributions
of and differences between multiple role clusters should be placed in the same
context, allowing domain experts to compare multiple role clusters
simultaneously (P2). Third, the interconversion and transition among the role
clusters should be intuitively visualized to enable domain experts to
understand the dynamic changes of informal roles. Particularly, tracking the
evolution of informal roles and how players shift their positions is necessary
for understanding the evolving gaming experiences of different players. Thus,
an intuitive way to visualize the interconversion and transition among the
roles is desired (P3).

Glyph Design. To meet the first and the third purpose (P1, P3), we design a
novel glyph to represent the informal role cluster, consisting of an inner
circle and an outer circle. Inspired by (Tsang et al., 2020), the inner circle
is a Polar coordinate where each white axis represents one metric, and the
color of the inner circle corresponds to a unique informal role. The glyph
size is determined by the player amount holding the informal role. We obtain
its average value for each metric and connect each metric to form a radar
chart that characterizes the informal role. The outer circle, i.e., a “planet
ring,” depicts the interconversion between informal roles within a snapshot.
We define that in snapshot $T$ (containing timestamps from $t_{i}$ to
$t_{i+N}$ and $N$ is the period of the snapshot), and interconversion between
informal roles occurs when a player switches its position from informal role
$A$ at timestamp $t_{i}$ ($i<N$) to another informal role $B$ at timestamp
($t_{i+1}$). For example, in Figure 6(c), each colored outer circle around the
inner circle indicates a transition, of which the color denotes the target
role cluster of the transition. The transition ratio (i.e., the amount of
interconversion players divided by the amount of all players in the informal
role) corresponds to the clockwise arc angle of the ring. To meet the second
purpose (P2), we follow a classic comparative visualization design, namely,
the superposition proposed by Gleicher et al. (Gleicher et al., 2011) to
visually compare two informal roles. Notably, we display an enlarged glyph
without the outer circle when users hover on a particular glyph, as shown in
Figure 6(c). A legend of metrics is labeled around the radar chart. Two radar
charts, i.e., the source and the destination informal role, are stacked on the
inner circle to enable a straightforward comparison.

Design Alternative of the Glyph. The final glyph undergoes several user-
centric design iterations. As shown in Figure 6, we initially designed a
circular grouped bar glyph to represent the role cluster. Each segment
represents one metric, and the bar charts on the top of each segment indicate
the value distribution of that metric. The size of the glyph represents the
number of players holding the role. The size of the red circle on the glyph
center indicates the number of players shifting their positions. Our experts
commented that this design did not use the inner space of the circle
efficiently, and the metric distribution is hard to compare with each other.

Figure 6. Design alternatives of informal role clusters. (a) uses bar height
to encode metric distribution and arrows to denote an interconversion between
two roles; (b) uses colors of the outer circle to encode metrics and inner
radar chart to indicate interconversion from other informal roles; (c) takes
radar chart to represent the metrics and ”planet rings” to tell the
interconversion to different informal roles.

Figure 6(b) borrows the design in (Tsang et al., 2020), where the color shade
of the outer ring denotes metric values, and the inner circle represents the
number of players transferring to this role cluster. This design is more
spatially efficient but suffers from an ineffective comparison of metrics due
to human’s insensitivity to colors. Our experts finally chose the last version
that appends interconversion to each informal role, avoiding drawing any
connection among role clusters. Furthermore, the radar chart is much more
distinguishable.

Figure 7. The individual interaction view visualizes how an ego interacts with
“1-degree” alters over the course of his/her gaming process. The event
distribution is shown by stacked bars and the interaction among player is
supported by a storyline-based metaphor. The red rectangle indicates the
current events corresponding to the selected player.

Transition Flow across Snapshots. While the role transition glyph conveys the
potential role shifting within a shorter period (i.e., $18$h), tracking the
long-term interconversion and evolution is also important. To this end, we
embed a transition flow in the role transition overview. First, we find out
the identity of each informal role and its transition across snapshots.
Specifically, we use the Jensen-Shannon Divergence (JS) (Manning and Schutze,
1999) to measure metric distribution similarity between two informal roles as
follows:

(3)
$JS(p||q)=\frac{1}{2}*KL(p||\frac{(p+q)}{2})+\frac{1}{2}*KL(q||\frac{(p+q)}{2}),$

where $KL$ is the KL Divergence and $p,q$ are the distributions to be measured
(same meaning as in Equation 1). It provides a symmetric measurement for
players’ metrics. For each informal role $i$, we consider the role $j$ with
the least JS value as $i$’s most similar informal role and depict the two
informal roles ($i$ and $j$) using the same color. For the case where the
least JS value between $i$ and $j$ is significantly large (e.g., remarkably
larger than a threshold), we consider that no existing informal role $j$ is
similar to the current role $i$. Thus, we just assign a new color to the
informal role $i$. We align players’ IDs to track their transition from an
earlier informal role to a latter one across two subsequent snapshots. As
shown in Figure 5, we use a Sankey diagram to present identity transition
across multiple snapshots. Each column stands for a snapshot. Each node
denotes an informal role within the snapshot, and each flow represents a
transition between informal roles across subsequent snapshots. By integrating
the Sankey diagram with the above glyphs, the subtle role change occurring
within a snapshot and a significant role change happening across multiple
snapshots can be simultaneously witnessed, allowing domain experts to
interactively identify the role change within one snapshot and track
consistent informal roles (i.e., rectangles with the same color) across
continuous snapshots.

###  6.2. Role Transition Projection

Role transition projection (Figure 5(c)) looks into the players’ position
within a specific informal role in detail to analyze interconversion and
evolution at the informal role level. It supports the comparison between
informal roles in a fine-grained manner. We project the embedding vector of
each player onto a 2D space using t-SNE projection. We choose t-SNE as the
dimensionality reduction technique since it demonstrates superiority in
producing a 2D projection that reveals meaningful insights about data such as
clusters and outliers (Kim et al., 2016). Also, it is more visually
interpretable than that eigenanalysis and more intuitive than multidimensional
scaling (MDS) depending on the distribution (Li et al., 2018). Between the two
spaces of the informal roles, we connect all nodes to their metrics in the
central axes, similar to the design of the parallel coordinate plot (PCP).
Purple circles would highlight the players occurring role changes, and their
metric changes could be easily witnessed through the axis values. Users can
lasso on the role transition projection view to select a specific subset of
players, and the corresponding content in the event interaction view will be
updated.

###  6.3. Event Interaction View

The event interaction view facilitates exploring players’ event sequences that
lead to specific role changes. Specifically, this view helps identify the
particular event(s) that explain the interconversion and transition among the
roles. In reality, the length of players’ event sequences within a period may
vary significantly. We compress those consecutive events into one event to
decrease the sequence length. Then, the compressed event sequences are fed
into the Doc2Vec model (Le and Mikolov, 2014) to generate the latent
representations. We project the latent representations of all event sequences
corresponding to the selected role change by t-SNE to visualize the fine-
grained patterns that drive the role change intuitively. In Figure 5(g), each
dot represents one event sequence, and their placements reflect their relative
similarities to help domain experts discover patterns. We further enhance the
event interaction view with a coxcomb glyph due to the effectiveness of glyphs
in facilitating visual comparison and pattern recognition to better identify
the differences between clusters and find representative event sequence dots.
Particularly, we use event distribution to differentiate and characterize the
dots. Accordingly, the outer sectors in our coxcomb glyph design represent the
number of each event. However, the glyph design may bring about a visual
clutter when many dots exist. Similar to the strategy in (Zhao et al., 2017),
to alleviate this issue, we decrease the glyph opacity in default to ensure
that individual glyphs can be quickly inspected. When domain experts hover
over one glyph, it would be brought to the foreground. We also provide
interactions like panning and zooming for focusing on a particular region.
Regarding design alternatives, we did not select classic star/radar glyphs to
encode the event number distribution because the number of events is not
always the same for each event sequence pattern. Meanwhile, the line in the
star/radar glyphs is challenging to perceive when the color saturation is low
and the glyphs are small.

###  6.4. Individual Interaction View

To provide a detailed inspection and explanation of event patterns, the
individual interaction view summarizes and visualizes individual players’
interaction gaming process with other counterparts (R5). The input of the
individual interaction view includes the selected player and his/her friends
in all interaction rounds.

Visual Encoding for Interactive Events. We harness a stacked bar chart and
arrange the stacked bars over time to visualize the percentage of each event
in a particular period, i.e., $10$ minutes, demonstrating how an individual
player interacts with other counterparts. Specifically, the selected player is
considered “ego”, and other counterparts are “alters”, forming an “ego-alters”
social network. Initially, we compared the stacked bar design with the pie
charts. However, due to the dense space between two subsequent periods and the
fact that there are no considerable differences in the ratio of different
events, it is hard to obtain a precise distribution on a small pie. Therefore,
we finally chose the stacked bar design.

Visual Encoding for Individual Interaction. In the individual interaction
view, we align each interaction process (i.e., $10$-minute time period) along
the $x$-axis and show all interactions within two-time snapshots (i.e.,
$2\times 18$-hour time period). However, determining the coordinates along the
x-axis of each interaction round is easy, while placing the $y$-coordinates of
each interaction round to minimize the number of potential link crossing is an
NP-hard problem and is thus difficult to obtain an exact solution. We,
therefore, use a heuristics approach following the idea of narrative
visualization (Li et al., 2017; Munroe, 2009). Suppose we have a series of
interaction rounds representing them as rounded rectangles (Figure 7). In each
rectangle, circles indicate the players that participate in this round, and
the background color refers to the snapshot when events happen. Precisely, we
first align the interaction rounds in the $x$-direction according to the round
id (i.e., the order of appearance). The $y$-coordinate of each interaction
round is adjusted to minimize the overall curvatures and intersections when
linking identical players across consecutive interaction rounds. Specifically,
the $y$-coordinate in one interaction round lies within the $y$-range of the
median of the players participating in this round. We sort the interaction
rounds in the $y$-direction to ensure that the densest ones (the ones
containing the most players) are as far away from each other as possible to
avoid possible visual clutter and maximize the utilization of the available
space. We denote all the players that participate in the interaction process
as $P=p_{0},...,p_{k}$ with the index of $k$ defining the order of their
appearance. We then calculate a temp-$y$-coordinate for each client as
$temp_{y}(p_{k})=Index(p_{k})*space$, in which space is a constant value
indicating a horizontal gap between two adjacent players. After determining
all the positions of interaction rounds and each player in every interaction
round, we visualize the players’ participation as shown in Figure 7. We link
identical players across subsequent interaction rounds by curves.

###  6.5. Metric Statistics

In the role transition view and role transition projection, we use abstract
graph metrics to describe the characteristics of the detected informal roles.
Although these metrics facilitate our exploration of the informal roles, they
are hard to interpret from the perspective of game designs due to the lack of
semantics. In other words, the experts cannot go further to find the shared
game characteristics of informal roles (R5). To address this concern, we
design a novel metric statistics glyph to include in-game statistics in the
same context. Specifically, the in-game metrics include cash (Cas), grade
(Gra), intimacy (Int), and combat score (Com), and the glyph summarizes both
the informal role’s information and a specific player’s information as
follows.

Figure 8. Metric statistics. The left side draws in-game metrics by a “box
plot” along the circles, where the filled ring denotes the range and the
hollow arc represents the interquantile range (IQR). The right side shows the
distribution of abstract graph metrics on axes. The information of an
individual player is also drawn by a link (left) and a polygon (right). This
view supports a comparison between two selected informal roles and individual
players. Figure 9. (a) shows the low transition percentage and (g) shows that
players’ overall combat score increases over time. (b) and (c) are the
beginning and ending of a transition. Their metrics are shown by a radar
chart. In (d), in-game metrics and abstract graph metrics of two informal
roles are visualized and compared. (e) is the lassoed dots and the
corresponding players are shown in the event interaction view. (f) are
aggregated dots where players show similar event patterns.

Informal Role’s Information. Each dashed ring denotes a metric, where a box
plot presents the distribution of players’ information on that metric. The
color ring stands for the data range, and the hollow arc represents the box
plot’s interquartile range (IQR). In this way, the region of the stacked color
ring has a deeper color, and the difference between two IQRs can be observed,
which is convenient to capture the similarity between the ranges and the
difference between the two data distributions. At the right side of Figure 8,
a radar chart shows the distribution of an informal role’s metrics. The area
along the axis shows the metric distribution.

Individual’s Information. We propose different designs on the left and right
sides for an individual player. In the left circles, a line in the rings
indicates its value. We connect them by arcs. In the right chart, a polygon is
constructed by connecting metric values on all axis and the center of the
circle. Therefore, a significant visual difference can reflect data
differences in one dimension.

###  6.6. Interaction Among the Views

Apart from the most defining capabilities of RoleSeer, rich interactions are
integrated to catalyze an efficient in-depth analysis, following the design
mantra “overview first, zoom and filter, then details-on-demand”. First, the
general information in the network overview helps users identify time segments
of interest. Otherwise, they may get lost by the unexpanded columns in the
role transition overview. Then users can move to the specific timestamp, and
the role transition overview allows to expand multiple columns: 1) within a
timestamp, users can hover a glyph to observe a cluster-level metric
statistics, or click a glyph to move to the individual-level event &
individual view; 2) across timestamps, users can click a link between columns
to move to the individual-level role transition projection, in which lassoing
nodes shows the graph metric transformation in a group level. Furthermore, the
metric statistics show the game metric statistics at a group level. In the
event & individual interactive view, clicking a node shows the corresponding
player’s information from three perspectives in a link + view manner: 1) the
event & individual interactive view shows the event narrative; 2) the role
transition projection shows the metric transformation; 3) the metric
statistics shows the information of game characteristics.

Figure 10. From the perspectives of different egos (red arrows) in one
community, we observe a “closeness” drop (a) when the specific player is
offline (b).

##  7\. Evaluation

We evaluated the effectiveness of RoleSeer in multiple ways from the
visualization community (Isenberg et al., 2013; Lam et al., 2011). First, we
described two case studies with our domain experts (E1 – E4) who had
participated in our user-centric design process. Second, we invited $12$
participants who had no exposure to our system and conducted a user study to
further assess the potency of RoleSeer. Finally, we conduct expert interviews
about their user experience and feedback to RoleSeer.

Figure 11. Within a continuous transition, one player at different snapshots
is found. In the role transition projection, there is a positive correlation
between transition direction and metric shift. In the event interactive view,
the event distribution indicates that carbon and battle leads to the different
transitions. In the individual interactive view, a shorter online time in the
next time snapshot is observed.

###  7.1. Case I: Identifying Players with Key Informal Roles

The following activities occurred when our collaborating experts analyzed the
players’ social behaviors to uncover an interesting social role a particular
player holds in the specific MMORPG.

Observing the overall stability of the entire social network. Our data analyst
E2 first moved to the network overview to get a general idea of the whole of
social network evolution status (Figure 9). He inspected the statistics
information in terms of all transition (Tran) percentages, the number of nodes
and edges to identify the snapshot that significant drops and increases occur,
as well as the distributions of graph metrics across all the snapshots,
including degree (Deg), page rank (PR), betweenness (Bet), leverage centrality
(Lev), within module degree (WMD), and closeness (Clo), adopted from the
literature (Li et al., 2018). He witnessed that the transitions rarely
occurred at each snapshot. Except for the snapshot “0 – 1”, he could not find
a significant visual difference in terms of the online players (i.e., nodes)
and their interactions (i.e., edges). He explained that at the initial release
of the game, some players were just attracted by the novelty but may be away
from the keyboard after a shallow gameplay taste. For the remaining metrics,
he found they changed as expected. For example, in the beginning, the
distribution of combat score (Com) was mainly concentrated in smaller values.
Later, the proportion of higher combat score became higher (Figure 9 (g)). He
concluded that the game runs stably as expected.

First impression on the identified informal roles. E2 was then curious about
our approach’s identified informal roles, so he moved to the role transition
overview. After a general observation of the view, he was pretty interested in
the most significant nodes and edges in the first snapshot, so he compared the
transition flows between the first and the second columns in the role
transition overview. The top flow has the most instances that experience the
transition, so he clicked on and expanded the space between the two columns
next to the transition. Then, he located the two informal roles involving this
transition and inspected them in detail regarding their metric distributions
(Figure 9(b)(c)). By comparing the radar charts within two glyphs, he implies
that (b) plays a more critical role than (c). Moreover, he clicks on (b) and
(c) in order and the metrics statistic updates. In Figure 9(d), the advantage
of (b) in grade and combat scores illustrates that players have more combat
experiences and capabilities. In the blue arc of cash in Figure 9(d), he could
see a shallow region, implying the intensive economic activities of the
corresponding players.

Exploring and understanding a transition. Inspired by the glyph that presents
an obvious transition pattern as shown in Figure 9, E2 clicked on the two
corresponding informal roles of interest, the transition link between them,
and the system updated the role transition projection and event interaction
view. The corresponding players and their interaction events were displayed.
Notably, E2 observed three sub-clusters within the informal role (b), and the
deep purple dots highlighted correspond to the players who experienced the
role transition. E2 found that most players moved from the cluster periphery
in the informal role (b) to more centric positions in the informal role (c).
He further lassoed one part of the dots (Figure 9(e)), highlighting the
corresponding event interaction glyphs. From the event interaction glyphs, he
identifies a collection of dots in which nodes within have a similar visual
appearance, and he just clicked on it one by one (Figure 9(f)). The
corresponding dot was also highlighted in the role transition projection view,
as shown by dots connected by the deep purple links, facilitating tracking the
player from its position within the informal role (b) to the metric value on
the left axis to another value on the corresponding right metric axis, and
finally to the position within the informal role (c). Furthermore, in the
individual interaction view, the detailed interactions of the player with its
egocentric community were shown so that E2 could observe how the player
interacted with his/her alters.

Identifying players holding key informal roles. In the individual interaction
view, E2 guessed that the ego and some alters were offline friends since they
shared a similar formation of their game nicknames (Figure 9(f)). From the
metric distribution of the ego (upper part in Figure 10(a)), E2 found that its
degree decreases a bit. Still, the closeness drops significantly, which was
caused by the offline event of one player (P) shown by the interaction event
sequence in the individual interaction view (upper part in Figure 10(b)).
Therefore, E2 proposed a hypothesis that the absence of P leads to the drop of
the ego’s closeness, and P held the informal role that maintains the closeness
of the community. To further validate this hypothesis, E2 explored another
sub-cluster in the role transition projection and identified another alter of
the community (red arrow in Figure 10(b)). The metric change of this alter was
similar to the previous ego (lower part in Figure 10(a)), and in its
individual interaction view, P was offline when the shift in closeness metric
occurred (lower part in Figure 10(b)). Thus, E2 concluded that P played the
informal role that preserves community closeness. “I am quite inspired by this
finding, as I only need to track this player who can maintain the stability of
the community.” Following the same procedure, E2 only needed to identify and
study the players holding a similar informal role with P to ensure the
stability of a particular game community.

###  7.2. Case II: Revealing Reasons Behind Role Transitions

This case demonstrates the efficiency of RoleSeer for tracking informal role
transitions and understanding the underlying reasons behind such shifts.

Identifying players experiencing transitions from community periphery to
center. E1 first took a look at the role transition overview and expanded the
first columns, i.e., the first snapshot. He compared two informal roles and
found a significant difference regarding its metric distribution, i.e., the
metrics after role transitions have an overall augment in all dimensions. For
further inspecting the details, E1 clicked on the two glyphs and the
transition ring to visualize more information in the role transition
projection and event interaction views. Particularly, in the role transition
projection view, he discovered that all players with transitions went from the
margin areas to the more centric regions. Correspondingly, all metric
distributions except for closeness rise to a higher level or stay unchanged.
Therefore, E1 proposed a hypothesis that when a player’s network metrics drop
or increase after a role transition, he/she shall shift the role to margin or
center areas.

Figure 12. The results about users’ perceptions on RoleSeer regarding its
workflow effectiveness and the convenience of visual design. $1-7$ represents
“strongly disagree” to “strongly agree” on each statement (Q1 – Q16).

Following the above role transition, E1 continued to track the role transition
in the following snapshot, so he clicked on the informal role (Figure 11). All
the transition flows connected to the roles in the following snapshot were
highlighted. He observed one transition flow with the most significant shifts,
so he just clicked on the two informal role glyphs (Figure 11) and the
transition flow for detailed examination. After exploring the event
interaction view, E1 found the same player as above. On the contrary, this
player shifted his position from the center to the margin area, and all
his/her network metric values dropped. Echoing the above hypothesis, E1
validated the conclusion by saying that “when a player’s all graph metrics
decrease or increase, he/she goes from the center/margin to the margin/center
region.”

Interaction events behind such role transitions. To further investigate the
reasons behind such role transitions, E1 compared the event distribution of
this player and observed that the decline in carbon and the increase in battle
jointly lead to such transitions. Respectively, what E1 found told that too
many battles (competition matters) might damage players’ social role while
sufficient carbons (cooperation matters) may benefit their social positions.

###  7.3. User Study

We also conducted a user study to further assess the potency of RoleSeer.
Specifically, we evaluate our system in two aspects: workflow effectiveness
and convenience of visual design.

Participants. We recruited $12$ participants to perform a user study ($4$
females, $8$ males, $age_{mean}=24$, $age_{sd}=5.196$). They are $11$ students
and a teacher from the majors of computer science and game entertainment in a
local university through word-of-mouth. We chose the participants with MMORPG
gameplay experiences for which they could provide us with more comprehensible
insights and help verify the system usability. After a brief introduction, all
participants could easily understand the background and motivation. The study
was conducted face-to-face, where the participants would take tutorials,
experience the system as they wished, and fill out the questionnaire. After
completing the user study, they would receive a $10 voucher for an online
e-commerce platform.

Tasks. Participants were asked to perform the following four tasks
sequentially, which guided them to go through RoleSeer’s pipeline. Task One
was to examine the overall evolution dynamic of the gameplay social network in
order to narrow down timestamps of interest for exploration. Task Two was to
understand the characteristics of the informal roles and trace their
evolutions across timestamps, i.e., identify a continuous role change, in the
role transition overview. Task Three was to determine the differences between
two informal roles and evaluate whether the role transition projection
facilitates comparison at a group level. Task Four was to describe an
individual player in his/her interaction behaviors and compare two players in
their gameplay characteristics. Finally, after finishing all tasks,
participants must complete a questionnaire with 7-point Likert scale questions
(Figure 12).

Procedure. The whole process for one participant lasted about $40$ minutes.
Before we introduced our system, all participants were acknowledged that their
reactions and feedback would be anonymously collected in the questionnaire. We
then conducted a tutorial session for about $10$ minutes, in which we
introduced the workflow of our system, explained all the interactions in each
view, and told participants the meanings of visual encodings. Then, we allowed
them to pre-operate the system and consult us for further questions for
another $10$ minutes. When the participants got ready, they were required to
conduct the tasks mentioned above with our system. Participants were also
asked to think aloud their ideas when performing all the tasks. After
finishing all the tasks, participants must complete a questionnaire with
7-point Likert scale questions. The measurements in the questionnaire included
$11$ items about our system’s workflow effectiveness and five items about the
convenience of visual design (Sun et al., 2020; Xia et al., 2019). Other
comments not involved in the questionnaire were also recorded for reference.

Results. The questionnaire results are presented in Figure 12. In general, our
system was highly rated. Specifically, in terms of workflow effectiveness, the
participants commented that RoleSeer shows sufficient information at a time
level (Q1), but the Tran is too low to be witnessed (Q2). P1 (female, age:
$21$) commented that “for the Nodes, Edges, and Tran, line chart may be a
better visualization.” The participants felt RoleSeer could clearly show the
characteristics of the informal roles (Q3), facilitate comparison (Q4), and
can help trace the long-term evolution of informal roles by tracking the
visualization of the Sankey-like chart (Q5). The participants all praised the
group-level comparison since it could fill the gap between the role-level and
the individual-level analysis (Q6, Q8). However, some participants thought
that the design of the metric statistics view did not help much in the
workflow, “it only presents some metric information for comparison” (P3, male,
age: $22$) (Q7). The participants also found it interesting to explore the
player’s sequential events and community, where they could find patterns
related to real-life (Q9, Q10, Q11). Regarding the convenience of visual
design, the participants agreed that while most of the benefits of RoleSeer
visualizations can be attained with a specific learning curve, they could
develop their paths to explore with the system (Q13). They all agreed that
RoleSeer is quite helpful in finding the roles of interest (Q12), inspecting
informal roles (Q15), tracing and understanding role changes (Q16). The
participants also thought that the workflow from the overall to the fine-
grained provided by the visualizations was intuitive and facilitated their
analysis (Q14).

###  7.4. Expert Interview

We conducted a semi-structured interview with the experts to evaluate RoleSeer
and to check whether our approach helps them inspect and understand the
informal roles in the MMORPG context.

System Performance. We asked the experts to evaluate the results of our back-
end engine, e.g., the characteristics of the identified informal roles since
we did not have labeled ground truth. E3 reported that “the abstract graph
metrics are insufficient to provide insights to concrete game design, but we
can see the differences by the in-game metrics,“ echoed by E4. Through the
clustering analysis, they confirmed that “the metric statistic effectively
help us dig out the concrete reasons that lead to role changes in a
comparative manner.“ In a word, RoleSeer provides game designers with more
concrete information to explain informal roles generated by the back-end
model. Furthermore, our manner demonstrates the capability of revealing
underlying interaction patterns that contribute to role changes are meaningful
compared to what the experts can identify manually, “the coverage of
behavioral patterns can facilitate sense-making of role changes and game
designs”.

Visual Design and Interaction. All the experts appreciated the ability of
RoleSeer to support the interactive exploration of the informal roles.
Conventionally, the game team needs to manually distinguish different levels
of players and visualize the social network at different time steps for
comparison. “We often spend days on the entire process since we need to
extract different players,” said E1. RoleSeer can display multiple time
snapshots in real-time, making it easier for the experts to interact with the
data instead of going through the raw dataset, and largely shortening the
analysis time. RoleSeer further streamlines the process as the experts no
longer need to randomly select players and specify the sudden changes in the
social network by hand. Instead, all relevant interaction patterns are
automatically retrieved from a collection of players who experience role
changes. E2 was satisfied with the system’s ability to extend the analysis
scale and achieve sufficient patterns behind different role changes, “it helps
me learn the significant behavioral patterns that play an important role in
the role change and quickly identify similar players to confirm my
assumptions”. With RoleSeer, the team was able to identify patterns for
typical role changes.

Takeaway. The experts are surer of the importance of a good social network for
games and players. Through RoleSeer, they find that players holding the
marginal informal roles are usually limited to specific behavioral patterns
such as a significant percentage of battle events, and only interacting with a
small number of players is not conducive to the development of a good social
network. “A good social network should be a network with a low proportion of
players holding the marginal informal roles,” said E2. The diversity of
interactions can “reduce the likelihood of loss due to certain broken social
relationships and improve the efficiency of integrating into roles, e.g., with
sufficient carbon events,” said E4. In addition, the experts further identify
that encouraging role-to-role changes is a sign of a good social network,
i.e., the “openness” of a social network can accommodate players’ role
changes, such as newcomers can gradually hold specific “core” informal roles.

Generalizability and Scalability. When discussing which component(s) of
RoleSeer can be directly generalized to other application scenarios and which
need(s) customization to explore the potentials of our system, two insights
were obtained: (1) Visual Design. The experts commented that our designs,
especially the role transition view and the individual interaction view are
pretty generic and can fit other similar real-world scenarios. (2) Data.
RoleSeer covers the typical data and logs that the MMORPGs can collect and do
not rely on particular data types, which can be easily modified to attach to
other existing MMORPGs’ social network analyses. In terms of scalability, we
preprocess the social network to multiple snapshots and conduct the
exploratory analysis at multiple levels. Furthermore, we do not directly
render the nodes and edges of the network; thus, we resolve the scalability
issue to some extent.

##  8\. Discussion

In this section, we discuss the high-level synthetics, the design
implications, and the limitations of our approach.

Contributions Over Previous Work. Previous studies suggest that a social
network with few marginal players and low churn risk should be stable. Our
work confirms that the social establishment of players has a clear correlation
with their subsequent retention, i.e., a rather persistent player social
network tends to have a low percentage of marginal nodes, and most members in
the network should have more than one social relationship. Our work also sheds
light on the possible reasons as to why the “decentralized” groups gradually
developed from small groups perform better in terms of stability and openness.
1) It is easy to establish multilateral relations within small groups (Li et
al., 2017). As a result, the proportion of marginal players is small, and the
overall stability is improved (Figure 2). 2) The social network of a small
group is inclined to accommodate newcomers, mainly reflected in two aspects: a
higher proportion of newcomers in the entire network; and the possibility for
newcomers to slowly change or develop their social roles into more central
positions. 3) Compared with the core group, the threshold for newcomers to be
integrated into a small group is lower (Li et al., 2017; Lu et al., 2019),
e.g., through a mentor-apprentice relationship or fighting against game
dungeons with existing members. Furthermore, because the network is developed
from multiple small groups, newcomers are more likely to hold relatively
central positions as the network develops (Li et al., 2017). 4) There exist
certain social roles (Ang and Zaphiris, 2010; Canossa et al., 2019; Williams
et al., 2014; Gladwell, 2006) in small group social networks to enhance the
gaming experience. A typical example is a social role “connector” (Gladwell,
2006) defined as the shared friend of multiple players who plays the role of
passing messages to a different community. Our work discovers a special type
of “connector”, i.e., “bridge-builder”, who also connects players from
different communities but better facilitates relationship establishment among
second-order friends (i.e., “alters”). The friends of “bridge-builder” are
likely to play together and interact with each other directly, as shown in
Figure 10. Thereby, a significant contribution of RoleSeer over the theory of
“connector” is that we further identify the role of “bridge-builder”, who is
critical in establishing friendship.

Implications for Promoting Socialization in MMORPGs. This work gives several
design implications for socialization promotion in the virtual world. 1)
Promote social establishment. This study found that even if there is only one
social relationship with limited interactive events, e.g., playing in a
dungeon together, a player’s retention rate increases by $10$ – $20$% (Figure
1). This suggests the need to stimulate players to socialize, break the ice,
and establish the first pair of social relationships, possibly through
“participating in dungeons or missions with friends” or “guiding players to
interact with their friends”. 2) Promote the growth of the number of
relationships. In Figure 1, we found that if players can have $5$ – $7$
effective social relationships early in their game experiences, their
retention rate increases to more than $95$%. Thus, it is beneficial to
increase the number of social relationships a player has through methods such
as by adding an information push like “you have not participated in the
dungeon with your friend for a long time”. Also, it could be helpful to
encourage players to maintain social relationships actively, e.g., by
providing multiple pairs of mentor-apprentice relationships. 3) Promote the
integration of small groups. A “decentralized” social network established by
integrating small groups is more stable and accessible to newcomers than a
“centralized” social network guided by a core group (Li et al., 2017). This
could be achieved by approaches including “breaking up the fixed team” or
“introducing friends to other friends for rewards”.

Generalization to Beyond MMORPGs. RoleSeer could be applied to other scenarios
like user churn prediction. User churn denotes the phenomenon where users stop
using an online service, and early detection/prediction of churning is crucial
for the online system’s maintenance and development. When users begin to
leave, their daily activeness on the platform might gradually or sharply drop
and such status change may lead to informal role change (Yang et al., 2018).
RoleSeer could help identify the likelihood of user churn before it happens by
visualizing how the status of the social role changes over time. Following the
properly designed visual tips, the experts could easily track how different
social roles evolve, inspect their reasons, further correlate the individual
behaviors with user churn and explore how users’ ego community influences
their retention. Such retrospective analysis can assist experts in discovering
user churn patterns and diagnosing online service designs.

Limitation. This work has several limitations. First, we only worked with a
relatively small team of experts (E1 – E4) for the expert interview
evaluation, and we recruited a limited number of participants to evaluate the
workflow effectiveness and the efficiency of visual designs in a qualitative
manner. Second, we adopt only ten key metrics to characterize the detected
informal role cluster, and more metrics such as centrality and within module
degree mentioned by existing work (Bader and Hogue, 2003; Hirsch, 2005; Wu,
2008; Li et al., 2018) could be explored in the future. Third, this work only
focuses on one-step role change, i.e., inspecting the players’ interactive
behavior patterns from one informal role to another without considering a
sequence of role changes down a path in the player social network.

##  9\. Conclusion and Future Work

This study presents a framework that allows us to interactively inspect the
potential informal roles and explore the underlying patterns that lead to the
interconversion, transitions of roles in MMORPGs. Specifically, we first
propose a novel methodology based on structural-similarity-preserved network
embedding and alignment to computationally model each player. We then develop
four major visualizations that enable the output of the back-end model to be
quickly inspected at the overview, role cluster, and individual levels.
Empirical case studies and experts’ feedback verify the efficacy of our
approach. For future work, we will consider sequences of role changes that may
depict a collection of players’ entire trajectories in the game instead of
only focusing on role changes between two adjacent time snapshots. Also, we
will perform a systematic evaluation involving more experts.

###### Acknowledgements.

We are grateful for the valuable feedback and comments provided by the
anonymous reviewers. This work is partially supported by the research start-up
fund of ShanghaiTech University and the Research Grants Council of the Hong
Kong Special Administrative Region, China under Grant No. T44-707/16-N.

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