TY - JOUR
T1 - A comprehensive survey on community detection with deep learning
AU - Su, Xing
AU - Xue, Shan
AU - Liu, Fanzhen
AU - Wu, Jia
AU - Yang, Jian
AU - Zhou, Chuan
AU - Hu, Wenbin
AU - Paris, Cecile
AU - Nepal, Surya
AU - Jin, Di
AU - Sheng, Quan Z.
AU - Yu, Philip S.
PY - 2024/4
Y1 - 2024/4
N2 - Detecting a community in a network is a matter of discerning the distinct features and connections of a group of members that are different from those in other communities. The ability to do this is of great significance in network analysis. However, beyond the classic spectral clustering and statistical inference methods, there have been significant developments with deep learning techniques for community detection in recent years--particularly when it comes to handling high-dimensional network data. Hence, a comprehensive review of the latest progress in community detection through deep learning is timely. To frame the survey, we have devised a new taxonomy covering different state-of-the-art methods, including deep learning models based on deep neural networks (DNNs), deep nonnegative matrix factorization, and deep sparse filtering. The main category, i.e., DNNs, is further divided into convolutional networks, graph attention networks, generative adversarial networks, and autoencoders. The popular benchmark datasets, evaluation metrics, and open-source implementations to address experimentation settings are also summarized. This is followed by a discussion on the practical applications of community detection in various domains. The survey concludes with suggestions of challenging topics that would make for fruitful future research directions in this fast-growing deep learning field.
AB - Detecting a community in a network is a matter of discerning the distinct features and connections of a group of members that are different from those in other communities. The ability to do this is of great significance in network analysis. However, beyond the classic spectral clustering and statistical inference methods, there have been significant developments with deep learning techniques for community detection in recent years--particularly when it comes to handling high-dimensional network data. Hence, a comprehensive review of the latest progress in community detection through deep learning is timely. To frame the survey, we have devised a new taxonomy covering different state-of-the-art methods, including deep learning models based on deep neural networks (DNNs), deep nonnegative matrix factorization, and deep sparse filtering. The main category, i.e., DNNs, is further divided into convolutional networks, graph attention networks, generative adversarial networks, and autoencoders. The popular benchmark datasets, evaluation metrics, and open-source implementations to address experimentation settings are also summarized. This is followed by a discussion on the practical applications of community detection in various domains. The survey concludes with suggestions of challenging topics that would make for fruitful future research directions in this fast-growing deep learning field.
UR - http://www.scopus.com/inward/record.url?scp=85126334228&partnerID=8YFLogxK
UR - http://purl.org/au-research/grants/arc/DE200100964
U2 - 10.1109/TNNLS.2021.3137396
DO - 10.1109/TNNLS.2021.3137396
M3 - Article
C2 - 35263257
AN - SCOPUS:85126334228
SN - 2162-237X
VL - 35
SP - 4682
EP - 4702
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
IS - 4
ER -