KMS Of Academy of mathematics and systems sciences, CAS
| A Comprehensive Survey on Community Detection With Deep Learning | |
Su, Xing1; Xue, Shan1,2; Liu, Fanzhen1; Wu, Jia1; Yang, Jian1; Zhou, Chuan3 ; Hu, Wenbin4; Paris, Cecile2; Nepal, Surya2; Jin, Di5; Sheng, Quan Z.1; Yu, Philip S.6
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| 2022-03-08 | |
| Source Publication | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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| ISSN | 2162-237X |
| Pages | 21 |
| Abstract | 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. |
| Keyword | 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 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | Australian Research Council through the DECRA Project[DE200100964] |
| WOS Research Area | Computer Science ; Engineering |
| WOS Subject | Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
| WOS ID | WOS:000767842400001 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/60149 |
| Collection | 应用数学研究所 |
| Corresponding Author | Wu, Jia |
| Affiliation | 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 |
| Recommended Citation 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. |
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