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- Title
Community Detection Algorithm with Autoencoding-Like Modular Enhanced Non-Negative Matrix Factorization.
- Authors
ZHU Yulong; LIU Jianzhong; ZHANG Yinbao; ZHANG Xinjia; SONG Yongcheng; LIU Sicong; WANG Yabo
- Abstract
Community detection has been one of the key research directions in network analysis. Most of the current network community detection algorithms mainly use the structural information of the network to adopt a greedy algorithm to maximize a certain indicator, which cannot fully consider the node feature information, edge weight, and network community relationship asymmetry. To address this situation, this paper proposes an autoencoder-like modularity nonnegative matrix factorization (AMNMF) community detection algorithm. The algorithm expands the depth of non-negative matrix factorization by using an encoder-like structure, and introduces modularity and graph regularizer into the objective function optimization process of non-negative matrix factorization to fully mine the node and community structure information in the network. The problem of community relationship imbalance is solved by adding orthogonal constraints to the middle layer of the encoder. Experiments on multiple real networks show that: AMNMF is an effective NMF extension algorithm that uses node feature information and network structure information. Compared with the best results of baseline algorithms, it achieves an improvement of about 15% to 122%, and can accurately and effectively complete the community detection task.
- Subjects
MATRIX decomposition; NONNEGATIVE matrices; GREEDY algorithms; INFORMATION networks; ALGORITHMS; FACTORIZATION
- Publication
Journal of Computer Engineering & Applications, 2024, Vol 60, Issue 11, p258
- ISSN
1002-8331
- Publication type
Article
- DOI
10.3778/j.issn.1002-8331.2305-0108