A Comprehensive Survey on Community Detection With Deep Learning

doi:10.1109/TNNLS.2021.3137396

CSpace > 应用数学研究所

	A Comprehensive Survey on Community Detection With Deep Learning
	Su, Xing 1; Xue, Shan 1,2; Liu, Fanzhen 1; Wu, Jia 1; Yang, Jian 1; Zhou, Chuan3 ; Hu, Wenbin 4; Paris, Cecile 2; Nepal, Surya 2; Jin, Di 5; Sheng, Quan Z.1; Yu, Philip S.6
	2022-03-08
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
页码	21
摘要	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.
关键词	Deep learning Taxonomy Optimization Partitioning algorithms Clustering algorithms Social networking (online) Peer-to-peer computing Community detection deep learning graph neural network network representation social networks
DOI	10.1109/TNNLS.2021.3137396
收录类别	SCI
语种	英语
资助项目	Australian Research Council through the DECRA Project[DE200100964]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000767842400001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/60149
专题	应用数学研究所
通讯作者	Wu, Jia
作者单位	1.Macquarie Univ, Sch Comp, Sydney, NSW 2109, Australia 2.CSIRO Data61, Sydney, NSW 2015, Australia 3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100093, Peoples R China 4.Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China 5.Tianjin Univ, Sch Comp Sci & Technol, Tianjin 300350, Peoples R China 6.Univ Illinois, Dept Comp Sci, Chicago, IL 60607 USA
推荐引用方式 GB/T 7714	Su, Xing,Xue, Shan,Liu, Fanzhen,et al. A Comprehensive Survey on Community Detection With Deep Learning[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2022:21.
APA	Su, Xing.,Xue, Shan.,Liu, Fanzhen.,Wu, Jia.,Yang, Jian.,...&Yu, Philip S..(2022).A Comprehensive Survey on Community Detection With Deep Learning.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,21.
MLA	Su, Xing,et al."A Comprehensive Survey on Community Detection With Deep Learning".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022):21.