short-paper

How Significant Attributes are in the Community Detection of Attributed Multiplex Networks

Authors:

Yong TangAuthors Info & Claims

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 2057 - 2061

https://doi.org/10.1145/3539618.3591998

Published: 18 July 2023 Publication History

Abstract

Existing community detection methods for attributed multiplex networks focus on exploiting the complementary information from different topologies, while they are paying little attention to the role of attributes. However, we observe that real attributed multiplex networks exhibit two unique features, namely, consistency and homogeneity of node attributes. Therefore, in this paper, we propose a novel method, called ACDM, which is based on these two characteristics of attributes, to detect communities on attributed multiplex networks. Specifically, we extract commonality representation of nodes through the consistency of attributes. The collaboration between the homogeneity of attributes and topology information reveals the particularity representation of nodes. The comprehensive experimental results on real attributed multiplex networks well validate that our method outperforms state-of-the-art methods in most networks.

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Cited By

Zhang AWang XZhao YNejdl WAuer SKarras OCha MMoens MNajork M(2025)HACD: Harnessing Attribute Semantics and Mesoscopic Structure for Community DetectionProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3703540(616-624)Online publication date: 10-Mar-2025
https://dl.acm.org/doi/10.1145/3701551.3703540
Guo TPeng SZhang ZYang MLam KHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Improving the Accuracy of Locally Differentially Private Community Detection by Order-consistent Data PerturbationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657836(1743-1752)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657836

Index Terms

How Significant Attributes are in the Community Detection of Attributed Multiplex Networks
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
    2. Machine learning approaches
      1. Learning in probabilistic graphical models
        Latent variable models
      2. Neural networks

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Published In

cover image ACM Conferences

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2023

3567 pages

ISBN:9781450394086

DOI:10.1145/3539618

General Chairs:
Hsin-Hsi Chen
National Taiwan University
,
Wei-Jou (Edward) Duh
National Taiwan University
,
Hen-Hsen Huang
Academia Sinica
,
Program Chairs:
Makoto P. Kato
Spotify
,
Josiane Mothe
Universite de Toulouse
,
Barbara Poblete
University of Chile and Amazon Visiting Academic

Copyright © 2023 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 July 2023

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National Natural Science Foundation of China

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SIGIR '23

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SIGIR

SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 23 - 27, 2023

Taipei, Taiwan

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Zhang AWang XZhao YNejdl WAuer SKarras OCha MMoens MNajork M(2025)HACD: Harnessing Attribute Semantics and Mesoscopic Structure for Community DetectionProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3703540(616-624)Online publication date: 10-Mar-2025
https://dl.acm.org/doi/10.1145/3701551.3703540
Guo TPeng SZhang ZYang MLam KHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Improving the Accuracy of Locally Differentially Private Community Detection by Order-consistent Data PerturbationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657836(1743-1752)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657836

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