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1. A Survey of Community Detection
Approaches: From Statistical Modeling
to Deep Learning
Abstract
Community detection, a fundamental task for network analysis, aims to
partition a network into multiple sub
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 i
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
A Survey of Community Detection
Approaches: From Statistical Modeling
to Deep Learning
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
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
A Survey of Community Detection
Approaches: From Statistical Modeling
Community detection, a fundamental task for network analysis, aims to
structures to help reveal their latent
functions. Community detection has been extensively studied in and broadly
world network problems. Classical approaches to
community detection typically utilize probabilistic graphical models and adopt
nfer 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

2. 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.