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Fast unfolding of communities in large networks: 15 years later

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arxiv 2311.06047 v1 pith:4C2J64US submitted 2023-11-10 physics.soc-ph cs.SI

Vincent Blondel , Jean-Loup Guillaume , Renaud Lambiotte This is my paper · ORCID
classification physics.soc-phcs.SI
keywords communitiesmethodbeendetectionfastlargenetworksproposed
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The Louvain method was proposed 15 years ago as a heuristic method for the fast detection of communities in large networks. During this period, it has emerged as one of the most popular methods for community detection, the task of partitioning vertices of a network into dense groups, usually called communities or clusters. Here, after a short introduction to the method, we give an overview of the different generalizations and modifications that have been proposed in the literature, and also survey the quality functions, beyond modularity, for which it has been implemented.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 3 citations worldwide. Full citation record

  1. Co-Activation Graph Analysis of Safety-Verified and Explainable Deep Reinforcement Learning Policies

    cs.AI 2025-01 conditional novelty 5.0 of 10

    The authors apply co-activation graph analysis to state sets generated by model checking to rank neuron and feature importance in safe deep RL policies.

  2. Comparative Analysis of Community Detection Algorithms on the SNAP Social Circles Dataset

    cs.SI 2025-02 reject novelty 2.0 of 10

    A benchmark comparison of six community detection algorithms on a Facebook graph finds Louvain and Label Propagation strongest by internal metrics, with no ground-truth validation.

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