2021/01/03 by Di Jin, Zhizhi Yu, Jin, Di +13 · 12 citations
Computer Science · Physics and Astronomy · #Advanced Clustering Algorithms Research #Artificial Intelligence (cs.AI) #Complex Network Analysis Techniques #Data Visualization and Analytics #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #cs.AI #cs.LG #cs.SI #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.2101.01669
openalex publication_date 2021/01/03 · openalex created_date 2021/01/18 · arxiv created 2021/08/14 · arxiv updated 2021/08/17 · openalex updated_date 2026/07/28
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.