research-article

Overlapping community detection using seed set expansion

Authors:

Joyce Jiyoung Whang,

David F. Gleich, and

Inderjit S. DhillonAuthors Info & Claims

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

October 2013

Pages 2099 - 2108

https://doi.org/10.1145/2505515.2505535

Published: 27 October 2013 Publication History

Abstract

Community detection is an important task in network analysis. A community (also referred to as a cluster) is a set of cohesive vertices that have more connections inside the set than outside. In many social and information networks, these communities naturally overlap. For instance, in a social network, each vertex in a graph corresponds to an individual who usually participates in multiple communities. One of the most successful techniques for finding overlapping communities is based on local optimization and expansion of a community metric around a seed set of vertices. In this paper, we propose an efficient overlapping community detection algorithm using a seed set expansion approach. In particular, we develop new seeding strategies for a personalized PageRank scheme that optimizes the conductance community score. The key idea of our algorithm is to find good seeds, and then expand these seed sets using the personalized PageRank clustering procedure. Experimental results show that this seed set expansion approach outperforms other state-of-the-art overlapping community detection methods. We also show that our new seeding strategies are better than previous strategies, and are thus effective in finding good overlapping clusters in a graph.

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Cited By

Li DJu HSharma AZhang HSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Boosting Multitask Learning on Graphs through Higher-Order Task AffinitiesProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599265(1213-1222)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599265
Citraro SDe Deyne SStella MRossetti G(2023)Towards hypergraph cognitive networks as feature-rich models of knowledgeEPJ Data Science10.1140/epjds/s13688-023-00409-212:1Online publication date: 16-Aug-2023
https://doi.org/10.1140/epjds/s13688-023-00409-2
Niu YKong DLiu LWen RXiao J(2023)Overlapping community detection with adaptive density peaks clustering and iterative partition strategyExpert Systems with Applications10.1016/j.eswa.2022.119213213(119213)Online publication date: Mar-2023
https://doi.org/10.1016/j.eswa.2022.119213
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Index Terms

Overlapping community detection using seed set expansion
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis

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cover image ACM Conferences

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

October 2013

2612 pages

ISBN:9781450322638

DOI:10.1145/2505515

General Chairs:
Qi He
LinkedIn, USA
,
Arun Iyengar
IBM T.J. Watson Research Center, USA
,
Program Chairs:
Wolfgang Nejdl
L3S Research Center, Germany
,
Jian Pei
Simon Fraser University, Canada
,
Rajeev Rastogi
Amazon, India

Copyright © 2013 ACM.

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Published: 27 October 2013

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CIKM'13: 22nd ACM International Conference on Information and Knowledge Management

October 27 - November 1, 2013

California, San Francisco, USA

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Cited By

Li DJu HSharma AZhang HSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Boosting Multitask Learning on Graphs through Higher-Order Task AffinitiesProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599265(1213-1222)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599265
Citraro SDe Deyne SStella MRossetti G(2023)Towards hypergraph cognitive networks as feature-rich models of knowledgeEPJ Data Science10.1140/epjds/s13688-023-00409-212:1Online publication date: 16-Aug-2023
https://doi.org/10.1140/epjds/s13688-023-00409-2
Niu YKong DLiu LWen RXiao J(2023)Overlapping community detection with adaptive density peaks clustering and iterative partition strategyExpert Systems with Applications10.1016/j.eswa.2022.119213213(119213)Online publication date: Mar-2023
https://doi.org/10.1016/j.eswa.2022.119213
Chen GZhou S(2023)A novel overlapping community detection strategy based on Core-Bridge seedsInternational Journal of Machine Learning and Cybernetics10.1007/s13042-023-02020-315:6(2131-2147)Online publication date: 4-Dec-2023
https://doi.org/10.1007/s13042-023-02020-3
Samie MBehbood EHamzeh A(2023)Local community detection based on influence maximization in dynamic networksApplied Intelligence10.1007/s10489-022-04403-553:15(18294-18318)Online publication date: 27-Jan-2023
https://doi.org/10.1007/s10489-022-04403-5
Garvardt JKomusiewicz CSommer F(2023)The Parameterized Complexity of s-Club with Triangle and Seed ConstraintsTheory of Computing Systems10.1007/s00224-023-10135-x67:5(1050-1081)Online publication date: 12-Aug-2023
https://doi.org/10.1007/s00224-023-10135-x
Krasanakis EPapadopoulos SKompatsiaris I(2023)AutoGF: Runtime Graph Filter Tuning for Community Node RankingComplex Networks and Their Applications XI10.1007/978-3-031-21131-7_15(189-202)Online publication date: 26-Jan-2023
https://doi.org/10.1007/978-3-031-21131-7_15
Held M(2022)Know thy tools! Limits of popular algorithms used for topic reconstructionQuantitative Science Studies10.1162/qss_a_002173:4(1054-1078)Online publication date: 20-Dec-2022
https://doi.org/10.1162/qss_a_00217
Liakos PNtoulas ADelis A(2022)Uncovering Local Hierarchical Overlapping Communities at ScaleIEEE Transactions on Big Data10.1109/TBDATA.2019.29404508:2(432-445)Online publication date: 1-Apr-2022
https://doi.org/10.1109/TBDATA.2019.2940450
Bourhim SBenhiba LIdrissi M(2022)A Community-Driven Deep Collaborative Approach for Recommender SystemsIEEE Access10.1109/ACCESS.2022.323032310(131144-131152)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3230323
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