KMS Chongqing Institute of Green and Intelligent Technology, CAS
Multi-Constrained Embedding for Accurate Community Detection on Undirected Networks | |
Wang, Qingxian1; Liu, Xinyu1; Shang, Tianqi2; Liu, Zhigang3; Yang, Han1; Luo, Xin3 | |
2022-09-01 | |
摘要 | A Symmetric Non-negative Matrix Factorization (SNMF)-based network embedding model adopts a unique Latent Factor (LF) matrix for describing the symmetry of an undirected network, which reduces its representation ability to the target network and thus resulting in accuracy loss when performing community detection. To address this issue, this paper proposes a new undirected network embedding model, i.e., Alternating Direction Method of Multipliers (ADMM)-based, Modularity, Symmetry and Nonnegativity-constrained Embedding (AMSNE), which can be applicable to undirected, weighted or unweighted networks. It relies on two-fold ideas: a) Introducing the symmetry constraints into the model to correctly describe the symmetric of an undirected network without accuracy loss; and b) Adopting the ADMM principle to efficiently solve its constrained objective. Extensive experiments on eight real-world networks strongly evidence that the proposed AMSNE outperform several state-of-the-art models, making it suitable for real applications. |
关键词 | Symmetric matrices Symbols Matrix decomposition Context modeling Task analysis Periodic structures Electronic mail Community detection network embedding non-negative matrix factorization non-negative model and alternating direction method of multipliers |
DOI | 10.1109/TNSE.2022.3176062 |
发表期刊 | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING |
ISSN | 2327-4697 |
卷号 | 9期号:5页码:3675-3690 |
通讯作者 | Luo, Xin(luoxin21@cigit.ac.cn) |
收录类别 | SCI |
WOS记录号 | WOS:000852246800058 |
语种 | 英语 |