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sis 24(6):

Research Article

High-Order Local Clustering on Hypergraphs

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  • @ARTICLE{10.4108/eetsis.7431,
        author={Jingtian Wei and Zhengyi Yang and Qi Luo and Yu Zhang and Lu Qin and Wenjie Zhang},
        title={High-Order Local Clustering on Hypergraphs},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={11},
        number={6},
        publisher={EAI},
        journal_a={SIS},
        year={2024},
        month={12},
        keywords={Local Clustering, Hypergraphs, Conductance},
        doi={10.4108/eetsis.7431}
    }
    
  • Jingtian Wei
    Zhengyi Yang
    Qi Luo
    Yu Zhang
    Lu Qin
    Wenjie Zhang
    Year: 2024
    High-Order Local Clustering on Hypergraphs
    SIS
    EAI
    DOI: 10.4108/eetsis.7431
Jingtian Wei1, Zhengyi Yang1,*, Qi Luo1, Yu Zhang2, Lu Qin3, Wenjie Zhang1
  • 1: UNSW Sydney
  • 2: UNSW Canberra
  • 3: University of Technology Sydney
*Contact email: zhengyi.yang@unsw.edu.au

Abstract

Graphs are a commonly used model in data mining to represent complex relationships, with nodes representing entities and edges representing relationships. However, graphs have limitations in modeling high-order relationships. In contrast, hypergraphs offer a more versatile representation, allowing edges to join any number of nodes. This capability empowers hypergraphs to model multiple relationships and capture high-order information present in real-world applications. We focus on the problem of local clustering in hypergraphs, which computes a cluster near a given seed node. Although extensively explored in the context of graphs, this problem has received less attention for hypergraphs. Current methods often directly extend graph-based local clustering to hypergraphs, overlooking their inherent high-order features and resulting in low-quality local clusters. To address this, we propose an effective hypergraph local clustering model. This model introduces a novel conductance measurement that leverages the high-order properties of hypergraphs to assess cluster quality. Based on this new definition of hypergraph conductance, we propose a greedy algorithm to find local clusters in real time. Experimental evaluations and case studies on real-world datasets demonstrate the effectiveness of the proposed methods.

Keywords
Local Clustering, Hypergraphs, Conductance
Received
2024-12-04
Accepted
2024-12-04
Published
2024-12-04
Publisher
EAI
http://dx.doi.org/10.4108/eetsis.7431

Copyright © 2025 J. Wei et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NCSA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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