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arXiv Machine Learning

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.

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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
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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
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arXiv Machine Learning
Oct 2

T-ARC: Topology-Aware Randomized Clustering via Distributionally Robust Stochastic Block Models

The paper introduces T-ARC, a clustering algorithm that integrates topological information into the K‑means objective by coupling a data‑fidelity term with a graph‑cut penalty. The latent graph is modeled as a random realization from a Stochastic Block Model, whose parameter is optimized via Distributionally Robust Optimization, using a persistence‑based similarity matrix derived from zero‑dimensional persistent homology. Experiments on synthetic non‑convex data and Fashion‑MNIST subsets demonstrate that T‑ARC recovers latent topological structures and outperforms K‑means on curved and interleaved clusters while remaining competitive and more stable on real data.

By Serena Grazia De Benedictis, Andersen Ang, Nicoletta Del Buono, Flavia Esposito, Laura Selicato
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arXiv Machine Learning
Aug 19

Community Concealment from Graph Neural Networks

The paper introduces FCom‑DICE, a feature‑aware perturbation method that rewires influential edges and adjusts node features to hide a target community from graph neural network (GNN) inference. It shows that concealment effectiveness depends on boundary connectivity and feature similarity, and that FCom‑DICE outperforms structure‑only DICE on synthetic and real networks such as Facebook, Wikipedia, and Bitcoin Transactions while preserving key structural and feature properties.

By Dalyapraz Manatova, Pablo Moriano, L. Jean Camp
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arXiv Machine Learning
Aug 5

Learning and Clustering on Temporal Graphs: Principles, Primitives, and Pooling

arXiv:2608. 03696v1 Announce Type: new Abstract: This work focuses on the problem of learning on temporal graphs, with particular emphasis on the task of clustering: obtaining coarse-grained representations by aggregating information from nodes, edges, and temporal dynamics - a task related to pooling in machine learning on graphs, or community detection in network science.

By Nelson Aloysio Reis de Almeida Passos, Emanuele Carlini, Salvatore Trani
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arXiv Machine Learning
Oct 23

Optimal Clustering of Multi-layer Networks under Mixture Block Models

The paper investigates clustering of multi‑layer networks generated under a mixture multi‑layer stochastic block model (MMSBM). It derives the minimax optimal clustering error rate, expressed via Chernoff information between component edge‑probability distributions, and introduces a two‑stage estimator that achieves this rate even under extreme sparsity. The method is extended to count‑weighted networks using a mixture multi‑layer Poisson block model, with numerical experiments and real‑data analyses demonstrating its practical performance.

By Zhongyuan Lyu, Ting Li, Dong Xia
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