arXiv Machine Learning
Oct 3

Degree-Corrected Joint Matrix Factorization for Multilayer Community Detection

The paper introduces a degree‑corrected joint matrix factorization technique for detecting communities in multilayer networks. It uses a nonnegative symmetric matrix trifactorization that enforces disjoint, shared communities across layers while allowing each layer to have distinct connectivity patterns and node degrees. An efficient algorithm is presented and evaluated on a multilayer degree‑corrected stochastic block model, showing superior performance compared to existing methods.

By Alexandra Dache, Manon Rustin, Arnaud Vandaele, Nicolas Gillis
arXiv Machine Learning
2d ago

Community detection in complex networks via node similarity, graph representation learning, and hierarchical clustering

The paper introduces three hierarchical frameworks for community detection in complex networks, integrating linkage-based clustering, vertex proximity matrices, and graph representation learning. It evaluates 364 module combinations on synthetic and real datasets, finding that pipelines using Wasserman‑Faust, PPMI proximity, and DNGR representations match the performance of Leiden and Louvain while producing nested partitions. The approach excels on graphs with many small communities but is limited by its time complexity to networks of roughly 10,000 vertices on personal machines.

By {\L}ukasz Brzozowski, Grzegorz Siudem, Marek Gagolewski
arXiv Machine Learning
4d ago

Dual-Corrected Stochastic Block Model ($\text{DC}^2\text{SBM}$): A Generalized Structure Detection Method for Attributed Networks

The paper introduces the Dual-Corrected Stochastic Block Model (DC²SBM), a generative framework for community detection in attributed networks that decouples node activity into independent topological and attribute correction factors. By incorporating these factors into link‑generation and attribute‑generation channels and adding a global scaling parameter, the model preserves computational efficiency while improving detection accuracy, especially when topological signals are weak. Experiments on synthetic and real datasets demonstrate that DC²SBM outperforms existing methods and that a single shared activity factor is insufficient when topology and attributes are decoupled.

By Zhenhai Chang, Shuo Wang, Yumeng Liu, Wei Liu