A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning

By Di Jin, Zhizhi Yu, Pengfei Jiao, Shirui Pan, Philip S. Yu, Weixiong Zhang. IEEE Transactions on Knowledge and Data Engineering, 2021

383 citations recorded by the source corpus.

Community detection, a fundamental task for network analysis, aims to partition a network into multiple sub-structures to help reveal their latent functions. Community detection has been extensively studied in and broadly applied to many real-world network problems. Classical approaches to community detection typically utilize probabilistic graphical models and adopt a variety of prior knowledge to infer community structures. As the problems that network methods try to solve and the network data to be analyzed become increasingly more sophisticated, new approaches have also been proposed and developed, particularly those that utilize deep learning and convert networked data into low dimensional representation. Despite all the recent advancement, there is still a lack of insightful understanding of the theoretical and methodological underpinning of community detection, which will be critically important for future development of the area of network analysis. In this paper, we develop and present a unified architecture of network community-finding methods to characterize the state-of-the-art of the field of community detection. Specifically, we provide a comprehensive review of the existing community detection methods and introduce a new taxonomy that divides the existing methods into two categories, namely probabilistic graphical model and deep learning. We then discuss in detail the main idea behind each method in the two categories. Furthermore, to promote future development of community detection, we release several benchmark datasets from several problem domains and highlight their applications to various network analysis tasks. We conclude with discussions of the challenges of the field and suggestions of possible directions for future research.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/bf0ec473d8e4760f5f4f94ac3f76e86005dff516

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Chuan Zhou
2. Jia Wu
3. Quan Z. Sheng
4. Shan Xue
5. Jian Yang
6. Philip S. Yu
7. Cécile Paris
8. Surya Nepal

In Computer Vision and Pattern Recognition

1. Jia Wu
2. Chuan Zhou
3. Philip S. Yu
4. Cécile Paris
5. Surya Nepal
6. Quan Z. Sheng
7. Di Jin
8. Charu C. Aggarwal

In Statistical and Nonlinear Physics

1. Philip S. Yu
2. Cécile Paris
3. Jia Wu
4. Surya Nepal
5. Quan Z. Sheng
6. Chuan Zhou
7. Shan Xue
8. Di Jin

Citations in the corpus

• A Comprehensive Survey on Community Detection With Deep Learning (2021, background): “…the patterns and functions inherent to communities [36], [37], existing surveys have mainly focused on certain aspects of community detection, including specific techniques [13], [14], [38]–[41], different network types [5], [12], [42], community types [6], [7], and application scenarios [10], [43].”
• From Local to Global: A Graph RAG Approach to Query-Focused Summarization (2024, methodology): “Given such a graph, a variety of community detection algorithms may be used to partition the graph into communities of nodes with stronger connections to one another than to the other nodes in the graph (e.g., see the surveys by Fortu-nato, 2010 and Jin et al., 2021).”
• Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval (2024, unknown): “Graph community retrieval has been a proven technique in various domains, such as social network analysis (Fortunato, 2010; Jin et al., 2021) and recommendation systems (Salha et al., 2019) for efficiently extracting relevant and contextual information from large-scale graphs.”