skip to main content
research-article

BiNeTClus: Bipartite Network Community Detection Based on Transactional Clustering

Published: 13 November 2020 Publication History

Abstract

We investigate the problem of community detection in bipartite networks that are characterized by the presence of two types of nodes such that connections exist only between nodes of different types. While some approaches have been proposed to identify community structures in bipartite networks, there are a number of problems still to solve. In fact, the majority of the proposed approaches suffer from one or even more of the following limitations: (1) difficulty in detecting communities in the presence of many non-discriminating nodes with atypical connections that hide the community structures, (2) loss of relevant topological information due to the transformation of the bipartite network to standard plain graphs, and (3) manually specifying several input parameters, including the number of communities to be identified. To alleviate these problems, we propose BiNeTClus, a parameter-free community detection algorithm in bipartite networks that operates in two phases. The first phase focuses on identifying an initial grouping of nodes through a transactional data model capable of dealing with the situation that involves networks with many atypical connections, that is, sparsely connected nodes and nodes of one type that massively connect to all other nodes of the second type. The second phase aims to refine the clustering results of the first phase via an optimization strategy of the bipartite modularity to identify the final community structures. Our experiments on both synthetic and real networks illustrate the suitability of the proposed approach.

References

[1]
Francesco Bonchi, Carlos Castillo, Aristides Gionis, and Alejandro Jaimes. 2011. Social network analysis and mining for business applications. ACM Trans. Intell. Syst. Technol. 2, 3, Article 22 (May 2011), 37 pages.
[2]
Jure Leskovec and Rok Sosič. 2016. SNAP: A general-purpose network analysis and graph-mining library. ACM Trans. Intell. Syst. Technol. 8, 1, Article 1 (July 2016), 20 pages.
[3]
Vincent D. Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. 2008. Fast unfolding of communities in large networks. J. Stat. Mech.: Theory Exp. 2008, 10 (2008), P10008.
[4]
Jaewon Yang and Jure Leskovec. 2014. Structure and overlaps of ground-truth communities in networks. ACM Trans. Intell. Syst. Technol. 5, 2, Article 26 (April 2014), 35 pages.
[5]
Taher Alzahrani and Kathy Horadam. 2016. Community detection in bipartite networks: Algorithms and case studies. In Complex Systems and Networks. Springer, 25--50.
[6]
David Melamed. 2014. Community structures in bipartite networks: A dual-projection approach. PLoS One 9, 5 (2014), e97823.
[7]
Mark E. J. Newman. 2004. Fast algorithm for detecting community structure in networks. Phys. Rev. E 69, 6 (2004), 066133.
[8]
Pascal Pons and Matthieu Latapy. 2005. Computing communities in large networks using random walks. In Proceedings of the International Symposium on Computer and Information Sciences. Springer, 284--293.
[9]
Usha Nandini Raghavan, Réka Albert, and Soundar Kumara. 2007. Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76, 3 (2007), 036106.
[10]
Michael J. Barber. 2007. Modularity and community detection in bipartite networks. Phys. Rev. E 76, 6 (2007), 066102.
[11]
Xin Liu and Tsuyoshi Murata. 2010. Community detection in large-scale bipartite networks. Inf. Media Technol. 5, 1 (2010), 184--192.
[12]
Xin Liu and Tsuyoshi Murata. 2010. An efficient algorithm for optimizing bipartite modularity in bipartite networks. J. Adv. Comput. Intell. Intell. Inf. 14, 4 (2010), 408--415.
[13]
Tsuyoshi Murata. 2009. Detecting communities from bipartite networks based on bipartite modularities. In Proceedings of the International Conference on Computational Science and Engineering (CSE’09), Vol. 4. IEEE, 50--57.
[14]
Paola Gabriela Pesantez and Ananth Kalyanaraman. 2019. Efficient detection of communities in biological bipartite networks. IEEE/ACM Trans. Comput. Biol. Bioinf. 16, 1 (2019), 258--271.
[15]
Michael J. Barber and John W. Clark. 2009. Detecting network communities by propagating labels under constraints. Phys. Rev. E 80, 2 (2009), 026129.
[16]
Daniel B. Larremore, Aaron Clauset, and Abigail Z. Jacobs. 2014. Efficiently inferring community structure in bipartite networks. Phys. Rev. E 90, 1 (2014), 012805.
[17]
Kenta Suzuki and Ken Wakita. 2009. Extracting multi-facet community structure from bipartite networks. In Proceedings of the International Conference on Computational Science and Engineering (CSE’09), Vol. 4. IEEE, 312--319.
[18]
Philipp Schuetz and Amedeo Caflisch. 2008. Efficient modularity optimization by multistep greedy algorithm and vertex mover refinement. Phys. Rev. E 77, 4 (2008), 046112.
[19]
Philipp Schuetz and Amedeo Caflisch. 2008. Multistep greedy algorithm identifies community structure in real-world and computer-generated networks. Phys. Rev. E 78, 2 (2008), 026112.
[20]
Brian Karrer and Mark E. J. Newman. 2011. Stochastic blockmodels and community structure in networks. Phys. Rev. E 83, 1 (2011), 016107.
[21]
Ulrik Brandes and Thomas Erlebach. 2005. Network Analysis: Methodological Foundation. Springer-Verlag Berlin Heidelberg.
[22]
Yongcheng Xu and Ling Chen. 2014. Modularity density for evaluating community structure in bipartite networks. Open Autom. Contr. Syst. J. 6 (2014), 684--691.
[23]
Mark E. J. Newman and Michelle Girvan. 2004. Finding and evaluating community structure in networks. Phys. Rev. E 69, 2 (2004), 026113.
[24]
Thomas S. Rask, Daniel A. Hansen, Thor G. Theander, Anders Gorm Pedersen, and Thomas Lavstsen. 2010. Plasmodium falciparum erythrocyte membrane protein 1 diversity in seven genomes–divide and conquer. PLoS Comput. Biol. 6, 9 (2010), e1000933.
[25]
Peter C. Bull, Caroline O. Buckee, Sue Kyes, Moses M. Kortok, Vandana Thathy, Bernard Guyah, José A. Stoute, Chris I. Newbold, and Kevin Marsh. 2008. Plasmodium falciparum antigenic variation. Mapping mosaic var gene sequences onto a network of shared, highly polymorphic sequence blocks. Molec. Microbiol. 68, 6 (2008), 1519--1534.
[26]
Li Chong, Kunyang Jia, Dan Shen, C. J. Richard Shi, and Hongxia Yang. 2019. Hierarchical representation learning for bipartite graphs. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI’19). 2873--2879.
[27]
Chih-Ming Chen, Chuan-Ju Wang, Ming-Feng Tsai, and Yi-Hsuan Yang. 2019. Collaborative similarity embedding for recommender systems. In Proceedings of the World Wide Web Conference (WWW’19). 2637--2643.
[28]
Zhang Yao, Yun Xiong, Xiangnan Kong, and Yangyong Zhu. 2017. Learning node embeddings in interaction graphs. In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM’17). 397--406.

Cited By

View all
  • (2024)Community-Based Task Assignment Method in Mobile Crowd SensingIEEE Access10.1109/ACCESS.2024.339565712(84387-84400)Online publication date: 2024
  • (2023)Relation-aware Graph Convolutional Networks for Multi-relational Network AlignmentACM Transactions on Intelligent Systems and Technology10.1145/357982714:2(1-23)Online publication date: 9-Jan-2023

Index Terms

  1. BiNeTClus: Bipartite Network Community Detection Based on Transactional Clustering

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Transactions on Intelligent Systems and Technology
    ACM Transactions on Intelligent Systems and Technology  Volume 12, Issue 1
    Regular Papers
    February 2021
    280 pages
    ISSN:2157-6904
    EISSN:2157-6912
    DOI:10.1145/3436534
    Issue’s Table of Contents
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 13 November 2020
    Accepted: 01 September 2020
    Revised: 01 July 2020
    Received: 01 October 2019
    Published in TIST Volume 12, Issue 1

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. Bipartite networks
    2. community detection
    3. transactional clustering

    Qualifiers

    • Research-article
    • Research
    • Refereed

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)8
    • Downloads (Last 6 weeks)1
    Reflects downloads up to 17 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)Community-Based Task Assignment Method in Mobile Crowd SensingIEEE Access10.1109/ACCESS.2024.339565712(84387-84400)Online publication date: 2024
    • (2023)Relation-aware Graph Convolutional Networks for Multi-relational Network AlignmentACM Transactions on Intelligent Systems and Technology10.1145/357982714:2(1-23)Online publication date: 9-Jan-2023

    View Options

    Login options

    Full Access

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format.

    HTML Format

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media