arXiv：cs.LG（机器学习，全量分类）· Alexandra Dache, Manon Rustin, Arnaud Vandaele, Nicolas Gillis·· 7 小时前AI 评分28
基于度修正联合矩阵分解的多层网络社区检测方法
Degree-Corrected Joint Matrix Factorization for Multilayer Community Detection
AI 导读
研究者提出一种基于联合非负对称矩阵三因子分解的多层网络社区检测方法，通过约束因子矩阵使社区在各层间互斥且共享，同时允许每层保留各自的连接模式和节点度。该方法配套了高效求解算法，并在多层度修正随机块模型（MDCBM）上验证，能在多种条件下稳定检测社区，而现有 SOTA 方法常受限于结构假设。
正文
Abstract:Multilayer networks allow the modeling of interactions between the same entities across different contexts, such as temporal observations, varying settings, or interactions of different types. The goal of community detection in multilayer networks is to identify groups of nodes exhibiting similar connectivity patterns, which may vary across layers. We propose a method based on a joint nonnegative symmetric matrix trifactorization for community detection in multilayer networks, where each graph is approximated by a nonnegative symmetric matrix trifactorization. Our approach enforces constraints on the factor matrices so that communities are disjoint and shared across layers, while allowing each layer to have its own connectivity patterns and node degrees. This flexibility enables the model to capture both local and global structural variations across layers. We also develop an algorithm to efficiently solve this problem. We evaluate multilayer community detection methods using the multilayer degree-corrected stochastic block model (MDCBM), a flexible framework for generating realistic multilayer graphs with heterogeneous degrees and varying connectivity patterns. Experiments show that our method reliably detects communities across diverse regimes, whereas existing state-of-the-art approaches are often limited by restrictive structural assumptions.
| Subjects: | Social and Information Networks (cs.SI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.01361 [cs.SI] |
| (or arXiv:2610.01361v1 [cs.SI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.01361 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Alexandra Dache [view email]
[v1]
Thu, 1 Oct 2026 09:31:10 UTC (2,102 KB)
来源：arXiv：cs.LG（机器学习，全量分类） · arxiv.org