中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 数学与系统科学研究院
  3. 中国科学院数学与系统科学研究院
  4. 应用数学研究所
A Comprehensive Survey on Community Detection With Deep Learning

文献类型：期刊论文

作者Su, Xing6; Xue, Shan5,6; Liu, Fanzhen6; Wu, Jia6; Yang, Jian6; Zhou, Chuan4; Hu, Wenbin3; Paris, Cecile5; Nepal, Surya5; Jin, Di2
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2022-03-08
页码21
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
ISSN号2162-237X
DOI10.1109/TNNLS.2021.3137396
英文摘要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.
资助项目Australian Research Council through the DECRA Project[DE200100964]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000767842400001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/60148]  
专题应用数学研究所
通讯作者Wu, Jia
作者单位1.Univ Illinois, Dept Comp Sci, Chicago, IL 60607 USA
2.Tianjin Univ, Sch Comp Sci & Technol, Tianjin 300350, Peoples R China
3.Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China
4.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100093, Peoples R China
5.CSIRO Data61, Sydney, NSW 2015, Australia
6.Macquarie Univ, Sch Comp, Sydney, NSW 2109, Australia
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.

入库方式： OAI收割

来源：数学与系统科学研究院

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。