← Back to all news
arXiv Machine Learning By {\L}ukasz Brzozowski, Grzegorz Siudem, Marek Gagolewski

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

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

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
benchmarks
More like this →
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
benchmarks
More like this →
arXiv Machine Learning
Sep 11

SynCo: Synthetic Community-Aware Attributed Graph Generator for Graph Neural Network Benchmarking

SynCo is a synthetic graph generator that lets users control node degree distributions and sub‑community structures, addressing limitations of existing generators that rely on power‑law distributions and lack flexibility. It is evaluated on graph mimicking, hyperparameter tuning, and node clustering, outperforming state‑of‑the‑art methods while preserving original data distributions. SynCo can generate large graphs with up to 2.1 million nodes.

By Guilherme Henrique Messias, Mariana Caravanti de Souza, Sylvia Iasulaitis, Alan Dem\'etrius Baria Valejo
benchmarks
More like this →