Proceedings

A Relaxed Symmetric Non-negative Matrix Factorization Approach for Community Discovery (Extended Abstract)

Zhigang Liu, Hao Yan, Yurong Zhong, Weiling Li

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Sister Conferences Best Papers. Pages 10916-10921.

https://doi.org/10.24963/ijcai.2025/1216

Keywords

  • Sister Conferences Best Papers: Machine Learning
  • Sister Conferences Best Papers: Knowledge Representation and Reasoning

Abstract

Community discovery is a prominent issue in com-plex network analysis. Symmetric non-negative matrix factorization (SNMF) is frequently adopted to tackle this issue. The use of a single feature matrix can depict network symmetry, but it limits its ability to learn node representations. To break this limitation, we present a novel Relaxed Symmetric NMF (RSN) approach to boost an SNMF-based community detector. It works by 1) expanding the representational space and its degrees of freedom with multiple feature factors; 2) integrating the well-designed equality-constraints to make the model well-aware of the network’s intrinsic symmetry; 3) employing graph regularization to pre-serve the local geometric invariance of the network structure; and 4) separating constraints from decision variables for efficient optimization via the principle of alternating-direction-method of multi-pliers. RSN’s effectiveness is verified through empirical studies on six real social networks, show-casing superior precision in community discovery over existing models and baselines.

Zhigang Liu, Hao Yan, Yurong Zhong, and Weiling Li. 2025. A Relaxed Symmetric Non-negative Matrix Factorization Approach for Community Discovery (Extended Abstract). In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 10916–10921. https://doi.org/10.24963/ijcai.2025/1216
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