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New algorithms improve community detection in hypergraphs

By PulseAugur Editorial · [4 sources] ·

Researchers have developed new spectral algorithms for community detection in hypergraphs, improving upon existing methods for non-uniform models. One paper introduces a three-step spectral algorithm that achieves partial recovery and weak consistency, particularly for sparse random hypergraphs with bounded expected degrees. Another paper establishes a sharp threshold for exact recovery in the general non-uniform hypergraph stochastic block model, proposing efficient algorithms that attain optimal performance. AI

IMPACT Advances in hypergraph community detection could lead to more sophisticated network analysis and pattern recognition in complex systems.

RANK_REASON Multiple academic papers published on arXiv detailing new algorithms and theoretical findings in the field of hypergraph community detection.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

New algorithms improve community detection in hypergraphs

COVERAGE [4]

  1. arXiv stat.ML TIER_1 English(EN) · Ioana Dumitriu, Hai-Xiao Wang, Yizhe Zhu ·

    Partial recovery and weak consistency in the non-uniform hypergraph Stochastic Block Model

    arXiv:2112.11671v4 Announce Type: replace-cross Abstract: We consider the community detection problem in sparse random hypergraphs under the non-uniform hypergraph stochastic block model (HSBM), a general model of random networks with community structure and higher-order interact…

  2. arXiv stat.ML TIER_1 English(EN) · Ioana Dumitriu, Hai-Xiao Wang ·

    Optimal and exact recovery on the general nonuniform Hypergraph Stochastic Block Model

    arXiv:2304.13139v4 Announce Type: replace-cross Abstract: Consider the community detection problem in random hypergraphs under the non-uniform hypergraph stochastic block model (HSBM), where each hyperedge appears independently with some given probability depending only on the la…

  3. arXiv stat.ML TIER_1 English(EN) · Sabyasachi Basu, Manuj Mukherjee, Lutz Oettershagen, Suhas Thejaswi ·

    Query-Limited Community Recovery in Stochastic Block Models

    arXiv:2606.02055v1 Announce Type: cross Abstract: We study exact community recovery in the two-community stochastic block model on $n$ vertices under limited and noisy access to network data. The learner may query a noisy neighborhood oracle that reveals each true neighbor of a q…

  4. arXiv stat.ML TIER_1 English(EN) · Suhas Thejaswi ·

    Query-Limited Community Recovery in Stochastic Block Models

    We study exact community recovery in the two-community stochastic block model on $n$ vertices under limited and noisy access to network data. The learner may query a noisy neighborhood oracle that reveals each true neighbor of a queried vertex independently with fixed probability…

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