发表机构
University of Mons（蒙斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多层网络社区检测，提出联合非负对称矩阵三分解方法，通过共享社区约束并允许层间差异，结合MDCBM评估，实验验证其优于现有方法。
AI 中文摘要
多层网络允许对同一实体在不同情境下的交互进行建模，例如时间观测、不同设置或不同类型的交互。多层网络中社区检测的目标是识别出表现出相似连接模式的节点组，这些模式可能在不同层之间有所变化。我们提出了一种基于联合非负对称矩阵三分解的方法，用于多层网络中的社区检测，其中每个图通过非负对称矩阵三分解来近似。我们的方法对因子矩阵施加约束，使得社区在各层之间是不相交且共享的，同时允许每一层拥有自己的连接模式和节点度数。这种灵活性使模型能够捕捉跨层的局部和全局结构变化。我们还开发了一种算法来高效地解决该问题。我们使用多层度校正随机块模型（MDCBM）来评估多层社区检测方法，该模型是一个灵活的框架，用于生成具有异构度数和不同连接模式的真实多层图。实验表明，我们的方法在各种场景下都能可靠地检测社区，而现有的最先进方法往往受限于严格的结构假设。
英文摘要
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.
