[1]Ghazal Kalhor

[1]School of Electrical and Computer Engineering, College of Engineering,
University of Tehran, Tehran, Iran

[2]Tehran Institute for Advanced Studies, Tehran, Iran

# Analysis of Research Trends in Computer Science: A Network Approach

<EMAIL_ADDRESS>Behnam Bahrak<EMAIL_ADDRESS>*  *

###### Abstract

Nowadays, computer science (CS) has emerged as a dominant force in numerous
research areas both within and beyond its own discipline. However, despite its
significant impact on scholarly space, only a limited number of studies have
been conducted to analyze the research trends and relationships within
computer science. In this study, we collected information on fields and
subfields from over 2,000 research articles published in the 2022 proceedings
of the top Association for Computing Machinery (ACM) conferences spanning
various research fields. Through a network approach, we investigated the
interconnections between CS fields and subfields to evaluate their
interdisciplinarity and multidisciplinarity. Our findings indicate that
computing methodologies and privacy and security stand out as the most
interdisciplinary fields, while human-centered computing exhibits the highest
frequency among the papers. Furthermore, we discovered that machine learning
emerges as the most interdisciplinary and multidisciplinary subfield within
computer science. These results offer valuable insights for universities
seeking to foster interdisciplinary research opportunities for their students.

###### keywords:

computer science, research field, association for computing machinery,
interdisciplinarity, multidisciplinarity

##  1 Introduction

In recent decades, computer science has emerged as a prominent field of
interest among scholars worldwide [1]. Numerous papers have been published
across various domains of computer science. Notably, interdisciplinary
research papers that employ computer science methodologies to address research
inquiries in diverse fields such as economics, biology, and sociology are
being accepted not only in computer science journals and conferences but also
in top-tier publication venues of other disciplines [2, 3, 4]. Therefore, many
researchers from other disciplines collaborate with computer scientists on
interdisciplinary projects to keep up with the latest changes in technology.

Several previous studies have analyzed networks of science to assess the
relationship between different fields of science in scholarly space. Karunan
et al. [5] explored the interdisciplinarity of the relationship between
biotechnology for energy and nanotechnology for energy fields. They utilized
citation networks to conduct this study. In [6], the authors proposed keyword
co-occurrence networks to detect closely-related research topics in computer
networking. For their analysis, they considered papers published by top venues
in this research area. Palchykov et al. [7] proposed the network of scientific
concepts and analyzed the structural features of this network, such as node
density and degree distribution. They based their study on arXiv manuscripts
and concepts elicited from the ScienceWISE.info platform. The authors of [8]
constructed the network of research fields using co-cited datasets from
different fields. They compiled a social science data archive for this study
and demonstrated the inclusion of non-social science fields, such as
information systems, artificial intelligence, and image processing, within
this network. Cunningham et al. [9] analyzed the multidisciplinarity of fields
of study among papers related to network science and the COVID-19 pandemic.
They constructed their field of study networks based on a heterogeneous
network of papers, authors, and fields.

In 2012, ACM proposed a new version of the classification system for computing
[10]. Cassel and Buzydlowski [11] utilized this classification to investigate
research patterns in computing from 1951 to 2017. They conducted time series
analysis to assess how these patterns have evolved over time. In [12], the
authors explored the co-authorship network of the Journal of Universal
Computer Science to quantify interdisciplinary collaboration between ACM
categories. They constructed a directed graph of computer science fields based
on the aforementioned network.

Building upon prior research, we narrow our focus to the fields and subfields
of computer science, aiming to illuminate their interactions and evaluate
their interdisciplinary and multidisciplinary characteristics within the
research landscape. Our study utilizes the ACM computing classification system
as a standardized framework to investigate the well-defined fields and
subfields of computer science. To the best of our knowledge, this is the first
paper to analyze the relationships between research fields in computer science
using a comprehensive dataset of different CS conferences, providing novel
insights into their interconnections. Moreover, we emphasize that our study
incorporates more recent data compared to previous works, allowing us to delve
into the latest trends and advancements in computer science research.

Our contributions can be summarized as follows:

  1. 1.

We investigate the distribution of computer science fields and subfields
within the papers published by ACM.

  2. 2.

We build the CS fields network and calculate centrality measures for its
nodes, including betweenness centrality, to identify the most
interdisciplinary and multidisciplinary research fields.

  3. 3.

We examine the relationships between computer science subfields by analyzing
the structural characteristics of the CS subfields network.

  4. 4.

We discuss various features within the identified communities of the CS
subfields network, including community size, prominent central nodes, and the
most frequently occurring ACM fields.

The remainder of this paper is organized as follows. In Section 2 section, we
explain the data collection process, as well as the methods and measures
utilized in this work. In Section 3, we illustrate our discoveries and provide
an analysis and interpretation of the obtained results. Finally, in Section 4,
we provide a summary of the paper and explore potential avenues for future
research.

##  2 Methodology

In this section, we provide an overview of the data collected for this study.
We describe the process of gathering the data and proceed to define the
network constructed based on the extracted information. Subsequently, we
outline the methods employed and the metrics calculated in our analysis.

###  2.1 Dataset

To collect the necessary data, we initially identified the top ACM conferences
in various research areas, utilizing the ranking provided by Research.com.
Through this process, we identified a total of nine conferences, which are
listed in Table 1.

Table 1: The best ACM conferences at different CS fields sorted by ranking.
Conference Name
---
Human Factors in Computing Systems
Computer and Communications Security
Knowledge Discovery and Data Mining
International Conference on Management of Data
International Symposium on Computer Architecture
Foundations of Software Engineering
Symposium on the Theory of Computing
International Conference on Multimedia Retrieval
Symposium on Applied Computing

From the proceedings of each conference in 2022, we gathered information
including the titles and computer science fields and subfields of the research
articles. The ACM digital library automatically classified the fields and
subfields of 98.95% (2083 out of 2095) of these papers. For the remaining 12
papers, we relied on the classification data provided by the authors in their
manuscripts, while the remaining papers either lacked classification
information or were not available online. An example of the conference data is
displayed in Table 2.

Table 2: An example of the conference data. Conference | CS Fields | Paper
---|---|---
CHI22 |  | Security and privacy/
---
Human-centered computing
| Understanding Privacy Switching Behaviour on
---
Twitter
KDD22 |  | Computing methodologies/
---
Mathematics of computing
| TARNet: Task-Aware Reconstruction for
---
Time-Series Transformer
|  |

Furthermore, we generated individual datasets for each computer science field,
consisting of the papers categorized within that particular field along with
their respective subfields. It is important to mention that the classification
of CS fields and subfields in our data aligns with the ACM computing
classification system. An example of the field data is shown in Table 3.

Table 3: An example of the field data (information systems). Conference | CS Subfields | Paper
---|---|---
CHI22 |  | Information retrieval/ Information
---
systems applications
What is Your Current Mindset?
KDD22 |  | Information retrieval/World
---
Wide Web
| Surrogate for Long-Term User Experience in
---
Recommender Systems
|  |

###  2.2 CS fields network

We constructed an undirected weighted network that visualizes the
relationships between computer science fields based on papers published by
ACM. In this network, each node represents a computer science field, and an
edge with weight $W$ connecting fields $u$ and $v$ indicates that there are
$W$ papers that belong to both fields $u$ and $v$.

###  2.3 CS subfields network

We constructed an undirected weighted network that represents the
relationships between computer science subfields based on papers published by
ACM. Each node in this network represents a computer science subfield, and an
edge with weight $W$ connecting subfields $u$ and $v$ indicates that there are
$W$ papers that belong to both subfields $u$ and $v$.

###  2.4 Disparity filter

We utilize the Disparity filter algorithm to decrease the network density by
eliminating insignificant ties, ensuring the preservation of its multi-scale
structure [13]. We utilize this technique to eliminate inconsequential edges
within the CS fields network.

###  2.5 Louvain community detection

The Louvain algorithm for community detection optimizes the modularity of a
network to identify communities by maximizing the difference between expected
and observed connections [14]. In our study, we employ this greedy approach to
uncover communities within the CS fields network.

###  2.6 Leiden community detection

The Leiden community detection algorithm is an enhanced version of the Louvain
algorithm that employs a fast local move approach. This algorithm refines the
partitions iteratively to guarantee the connectedness of the identified
communities. It exhibits improved computational efficiency and generates more
precise partitions in comparison with the Louvain algorithm [15]. We use this
algorithm to specify communities within the CS fields network.

###  2.7 Betweenness centrality

The betweenness centrality of each node in a network measures the frequency
with which a node lies on short paths between other pairs of nodes. The
formula for calculating betweenness centrality is as follows:

$\displaystyle B_{u}=\sum_{ij}{\frac{\sigma(i,u,j)}{\sigma(i,j)}},$ (1)

Where $\sigma(i,u,j)$ represents the number of shortest paths between nodes
$i$ and $j$ that pass through node or edge $u$, and $\sigma(i,j)$ represents
the total number of shortest paths between $i$ and $j$. The summation is
carried out over all pairs $i$ and $j$ of distinct nodes [16]. We employ this
metric to determine the level of interdisciplinarity among different fields
and subfields of computer science.

###  2.8 Weighted degree centrality

The weighted degree centrality of each node in a network is determined by the
sum of the weights of the edges connected to that node. The formula for
calculating weighted degree centrality is as follows:

$\displaystyle WD(u)=\sum_{v}{w(v,u)},$ (2)

where $v$ is a neighbor of $u$, and $w(v,u)$ represents the weight of the edge
connecting $v$ and $u$ [17]. We utilize this measure to assess the degree of
multidisciplinarity among various subfields of computer science.

##  3 Results and discussion

In this section, we present a detailed analysis of our findings and offer
insights into their interpretations.

###  3.1 Exploring CS subfields

In this part, we analyze the distribution of CS subfields to identify the most
popular research areas within each field of computer science. For this
purpose, we only consider the papers for which the subfields are specified.

Figure 1 illustrates the frequency of appearance of subfields within applied
computing among ACM papers. It shows that life and medical sciences is the
most common subfield in this area, followed by education and electronic
commerce. These findings support previous studies highlighting the
significance and prevalence of bioinformatics, education, and e-commerce as
interdisciplinary research topics in CS [18, 19, 20].

Figure 1: Bar plot of applied computing subfields.

Figure 2 presents the distribution of papers across different subareas of
computing methodologies and information systems. It reveals that machine
learning (ML) and artificial intelligence (AI) have the highest numbers of
papers. This observation aligns with the findings of Xu et al. [21], who
suggest that many computer scientists utilize AI and ML techniques in their
scholarly work. Additionally, Figure 2 shows that the majority of research
articles in the field of information systems focus on the applications of
these systems, confirming the findings of [22] that information systems are
applicable to various domains.

Figure 2: Bar plots of computing methodologies and information systems
subfields.

Figure 3 displays the distribution of papers among subfields of theory of
computation and mathematics of computing. The design and analysis of
algorithms has the highest frequency of research articles in the field of
theory of computation, consistent with previous studies [23] highlighting it
as the most common research theme among theoretical computer scientists.
Moreover, probability and statistics, along with discrete mathematics, are
prominent subfields. This result is expected since probability and statistics
serve as critical foundations for machine learning [24], and algorithms are
built upon concepts from discrete mathematics [25].

Figure 3: Bar plots of theory of computation and mathematics of computing
subfields.

Figure 4 showcases the distribution of papers among subfields of security and
privacy. Software and application security emerges as the dominant subarea,
which is understandable given that security and privacy play integral roles in
software systems [26].

Figure 4: Bar plot of security and privacy subfields.

Figure 5 presents the distribution of subfields within general and reference,
software and its engineering, and social and professional topics. Among
general and reference subfields, cross-computing tools and techniques has the
highest frequency. In software and its engineering, software creation and
management stands out as the prominent research area, while user
characteristics dominates the subfields of social and professional topics.

Figure 5: Bar plots of general and reference, software and its engineering,
and social and professional topics subfields.

Figure 6 depicts the bar plot of subfields within human-centered computing. It
shows that human-computer interaction (HCI) has the highest frequency,
aligning with previous studies [27] highlighting HCI as the dominant research
area among all subfields of computer science.

Figure 6: Bar plot of human-centered computing subfields.

Based on the distribution presented in Figure 7, emerging technologies is
identified as the most prevalent subfield within hardware. These hardware
technologies find wide applications in other subfields of CS, such as machine
learning [28, 29].

Figure 7: Bar plot of hardware subfields.

Figure 8 presents the distribution of subfields within networks subfields.
Network properties has the highest frequency, while other subfields within
networks subfields have fewer than 10 papers.

Figure 8: Bar plot of networks subfields.

Figure 9 showcases the distribution of subfields within computer systems
organization. Architectures emerges as the most common research theme in this
field of computer science, encompassing various types of computer
architectures, including serial, parallel, and distributed architectures.

Figure 9: Bar plot of computer systems organization subfields.

###  3.2 Analyzing CS fields network

In this part, we delve into the CS fields network to gain deeper insights into
the interconnections among various research fields in computer science. To
filter out insignificant edges, we employ the disparity filter algorithm [13],
resulting in a 24.62% reduction in network density (from 0.987 to 0.744).
Next, we calculate the betweenness centrality of each research area to
identify the most interdisciplinary field in computer science. Figure 10
demonstrates that the highest value of this metric is observed in the
computing methodologies and security and privacy subfields. This finding is
consistent with Aggarwal’s research [30], which emphasizes the broad
applicability of artificial intelligence, a prominent subfield within
computing methodologies, across various research domains. Additionally, our
previous study [31] revealed that computing methodologies is the prevailing
field of interest among computer science faculty members at top North American
universities. Moreover, Landau [32] highlights the extensive scope of security
and privacy, encompassing various areas within science, technology,
engineering, and mathematics (STEM). Thus, we can infer that research in the
area of security and privacy has increased among computer science scholars in
the last few years compared to previous decades [11].

In addition to betweenness centrality, we analyze the frequency of each field
among the research articles. Notably, human-centered computing emerges as the
field with the highest number of papers. This finding aligns with the study
conducted by Wania and colleagues [33], which highlights that human-computer
interaction (HCI) is a multidisciplinary area that bridges various fields of
computer science. Computing methodologies, on the other hand, secures the
second rank and is recognized as the most productive research field [34].

We further utilize the Louvain community detection algorithm [14] to identify
the communities within the CS fields network. As depicted in Figure 10, we
observe three distinct communities, characterized by their respective colors
(orange, blue, and pink), which can be interpreted as representing theory-
based, software-based, and hardware-based fields, respectively.

Figure 10: The CS fields network. Each node’s color indicates its community
membership. The size of a node corresponds to the frequency of its associated
field among papers, while the size of its label reflects its betweenness
centrality. The thickness of an edge indicates its weight.

###  3.3 Assessing CS subfields network

In this section, we analyze the CS subfields network to examine the
relationships between computer science subfields in the scholarly domain. The
network comprises 81 subfields connected by 640 weighted edges. It is a
connected network with a density of 0.1975. We calculate centrality metrics,
including weighted degree and betweenness centralities, to assess the
multidisciplinarity and interdisciplinarity levels of the nodes, respectively.
Figure 11 provides an overview of the CS subfields network. The subfields with
the highest values of weighted degree centrality are ML, HCI, and AI,
respectively. These findings align with previous studies [35, 33, 36],
suggesting that these subfields are highly multidisciplinary within computer
science. Furthermore, based on the betweenness centrality values, the most
interdisciplinary subfields are ML, AI, and architectures, in that order. This
finding is consistent with prior research [37, 38].

Figure 11: The CS subfields network. Each node is color-coded according to its
parent field. The size of a node reflects its weighted degree centrality,
while the size of its label represents its betweenness centrality. The
thickness of an edge signifies its weight.

Figure 12 presents the CS subfields network with the same configuration, but
with nodes colored based on the communities identified by the Louvain
community detection algorithm. The visualization shows six distinct
communities that have been detected. The statistical information of these
communities is presented in Table 4, which includes details such as community
size, density, the most central fields based on betweenness centrality, and
the most frequent computer science fields within each community.

Analyzing Table 4, we observe that theory of computation emerges as the most
common CS field in two different communities. This finding supports the notion
that theory of computation has broad applications in both hardware and
software engineering disciplines [39, 40].

Furthermore, according to the table, community 0 emerges as the largest
community identified by the Louvain algorithm, predominantly comprising
applied computing subfields. Within this community, machine learning takes the
spotlight as the most central node, which aligns with the extensive
utilization of machine learning techniques across diverse research domains
that extend beyond computer science [41]. Additionally, community 5 exhibits
the highest density and encompasses three fields primarily focused on computer
systems organization, with embedded and cyber-physical systems occupying a
central position. Moving on to community 1, it stands as the second largest
community, where HCI takes center stage as the most central node. Furthermore,
the parent field of HCI, human-centered computing, emerges as the most common
field within this community.

Figure 12: The CS subfields network, with communities detected via the Louvain algorithm. Table 4: Statistics of the Louvain communities within the CS subfields network. | Size | Density | Most Central Subfield | ACM Fields
---|---|---|---|---
0 | 21 | 0.4429 | Machine learning | Applied computing
1 | 18 | 0.4575 |  | Human computer
---
interaction (HCI)
Human-centered computing
2 | 16 | 0.4083 | Network security | Security and privacy
3 | 14 | 0.2967 | Architectures |  | Hardware, Theory of
---
computation
4 | 9 | 0.5833 |  | Software creation and
---
management
| Software and its engineering,
---
Theory of computation
5 | 3 | 1.0 |  | Embedded and
---
cyber-physical systems
| Computer systems
---
organization
|  |  |  |

Figure 13 depicts the CS subfields network with nodes colored according to the
communities identified by the Leiden community detection algorithm. Four
Leiden communities are detected within this network. An overview of
statistical features of these communities are provided in Table 5. The largest
community consists mostly of security and privacy subfields, and its most
central node is architectures. The highest density is for community 3 with
hardware as the prevailing field and machine learning as the most central
node. The second largest community (community 1) has data management systems
as the most central subfield and mathematics of computing and theory of
computation as most frequent fields.

Figure 13: The CS subfields network, with communities detected via the Leiden algorithm. Table 5: Statistics of the Leiden communities within the CS subfields network | Size | Density | Most Central Subfield | ACM Fields
---|---|---|---|---
0 | 29 | 0.3399 | Architectures | Security and privacy
1 | 21 | 0.3667 | Data management systems |  | Mathematics of computing,
---
Theory of computation
2 | 19 | 0.4737 |  | Human computer interaction
---
(HCI)
Human-centered computing
3 | 12 | 0.5606 | Machine learning | Hardware
|  |  |  |

##  4 Conclusion

In this study, we conducted a comprehensive analysis of computer science
fields and subfields using the latest ACM conference papers in this research
domain. Our investigation encompassed an assessment of the distribution of
subfields within each field, revealing HCI, ML, and AI as the dominant
subfields among the research articles. Furthermore, we examined the CS fields
network by calculating betweenness centrality values and analyzing the
frequency of its nodes. Our analysis highlighted computing methodologies and
privacy and security as the most interdisciplinary fields, as indicated by
their highest betweenness centrality scores. It is noteworthy that human-
centered computing exhibited the highest frequency among the papers.

Additionally, we employed the Louvain community detection algorithm to
classify nodes into three groups: theory-based, software-based, and hardware-
based fields. Furthermore, we constructed the CS subfields network to explore
the interconnectedness among various computer science subfields. Based on
weighted degree and betweenness centrality values, we identified machine
learning as the most multidisciplinary and interdisciplinary subfield within
computer science. Moreover, we applied both the Louvain and Leiden community
detection algorithms to uncover communities within the network and determined
the most central subfields and the most frequent fields within these
communities. Interestingly, the theory of computation emerged as the most
frequent field in two distinct Louvain communities, while architectures
occupied a central position in the largest Leiden community.

The findings of this study have practical implications for computer science
departments in universities, as they can leverage these insights to create
interdisciplinary research opportunities for students in fields like ML and
HCI. By facilitating collaboration with researchers from other disciplines,
universities can enhance the quality of research conducted by their students
substantially.

## Declarations

### Funding

No funding was received for conducting this study.

### Competing interests

The authors declare that they have no competing interests.

### Ethics approval

Not applicable.

### Consent to participate

Not applicable.

### Consent for publication

Not applicable.

### Availability of data and materials

The dataset generated and analyzed during the current study is available in
the CS Fields & Subfields Data repository, https://github.com/kalhorghazal/CS-
Fields-Subfields-Data.

### Authors’ contributions

All authors read and approved the final manuscript.

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