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Improved Community Detection using Stochastic Block Models

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arxiv 2408.10464 v2 pith:2YTPYVXQ submitted 2024-08-20 cs.SI

Minhyuk Park , Daniel Wang Feng , Siya Digra , The-Anh Vu-Le , George Chacko , Tandy Warnow This is my paper · ORCID
classification cs.SI
keywords communitynetworksapproachesblockclustersdetectionimprovemodifications
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Community detection approaches resolve complex networks into smaller groups (communities) that are expected to be relatively edge-dense and well-connected. The stochastic block model (SBM) is one of several approaches used to uncover community structure in graphs. In this study, we demonstrate that SBM software applied to various real-world and synthetic networks produces poorly-connected to disconnected clusters. We present simple modifications to improve the connectivity of SBM clusters, and show that the modifications improve accuracy using simulated networks.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. EC-SBM Synthetic Network Generator

    cs.SI 2025-02 conditional novelty 5.0 of 10

    EC-SBM is a scalable synthetic network generator that preserves cluster edge connectivity and degree sequence better than SBM or RECCS.

  2. Improved Community Detection using Stochastic Block Models

    cs.SI 2025-02 conditional novelty 4.0 of 10

    Well-Connected Clusters (WCC), a simple cut-removal postprocessor, improves the accuracy of SBM community detection on non-bipartite synthetic benchmarks.

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