skip to main content
10.1145/3071178.3071295acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
research-article

Community structure detection in multipartite networks: a new fitness measure

Published: 01 July 2017 Publication History

Abstract

Community structure detection algorithms are used to identify groups of nodes that are more connected to each other than to the rest of the network. Multipartite networks are a special type of network in which nodes are divided into partitions such that there are no links between nodes in the same partition. However, such nodes may belong to the same community, making the identification of the community structure of a multipartite network computationally challenging. In this paper, we propose a new fitness function that takes into account the information induced by existing links in the network by considering shadowed connections between nodes that have a common neighbor. The existence of a correct fitness function, i.e. one whose optimum values correspond to the community structure of the network, enables the design and use of optimization-based heuristics for solving this problem. We use numerical experiments performed on artificial benchmarks to illustrate the effectiveness of this function used within an extremal optimization based algorithm and compared to existing approaches. As a direct application, a multipartite network constructed from a direct marketing database is analyzed.

References

[1]
Gediminas Adomavicius and Alexander Tuzhilin. 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17, 6 (2005), 734--749. arXiv:3
[2]
Michael J Barber. 2007. Modularity and community detection in bipartite networks. Physical Review E 76, 6 (2007), 066102.
[3]
Santo Fortunate 2010. Community detection in graphs. Physics Reports 486, 3--5 (feb 2010), 75--174.
[4]
Santo Fortunato and Marc Barthelemy. 2007. Resolution limit in community detection. Proceedings of the National Academy of Sciences 104, 1 (2007), 36--41. arXiv:http://www.pnas.org/content/104/1/36.full.pdf+html
[5]
M. Kheirkhahzadeh, A. Lancichinetti, and M. Rosvall. 2016. Efficient community detection of network flows for varying Markov times and bipartite networks. Physical Review E - Statistical, Nonlinear, and Soft Matter Physics 93, 3 (2016).
[6]
Andrea Lancichinetti and Santo Fortunato. 2009. Benchmarks for testing community detection algorithms on directed and weighted graphs with over-lapping communities. Phys. Rev. E 80 (Jul 2009), 016118. Issue 1.
[7]
Andrea Lancichinetti, Santo Fortunato, and János Kertész. 2009. Detecting the overlapping and hierarchical community structure in complex networks. New Journal of Physics 11, 3 (2009), 033015.
[8]
Zhenping Li, Shihua Zhang, Rui-Sheng Wang, Xiang-Sun Zhang, and Luonan Chen. 2008. Quantitative function for community detection. Phys. Rev. E 77 (Mar 2008), 036109. Issue 3.
[9]
Xin Liu, Weichu Liu, Tsuyoshi Murata, and Ken Wakita. 2014. A framework for community detection in heterogeneous multi-relational networks. Advances in Complex Systems 17, 06 (2014), 1450018.
[10]
Y Liu, T Yang, L Fu, and J Liu. 2015. Community Detection in Multi-Partite Multi-Relational Networks Based on Information Compression. Journal of Computational Information Systems 11, 2 (2015), 693--700.
[11]
Rodica Ioana Lung, Mihai Suciu, and Noémi Gaskó. 2017. Noisy extremal optimization. Soft Computing 21, 5 (2017), 1253--1270.
[12]
Prakash Mandayam Comar, Pang-Ning Tan, and Anil K Jain. 2012. A framework for joint community detection across multiple related networks. Neurocomputing 76, 1 (2012), 93--104.
[13]
A. Miyauchi and N. Sukegawa. 2015. Maximizing Barber's bipartite modularity is also hard. Optimization Letters 9, 5 (2015).
[14]
Sérgio Moro, Paulo Cortez, and Paulo Rita. 2014. A data-driven approach to predict the success of bank telemarketing. Decision Support Systems 62 (2014), 22--31.
[15]
Mark EJ Newman and Michelle Girvan. 2004. Finding and evaluating community structure in networks. Physical review E 69, 2 (2004), 026113.
[16]
M. E. J. Newman. 2006. Modularity and community structure in networks. Proceedings of the National Academy of Sciences 103, 23 (2006), 8577--8582. arXiv:http://www.pnas.org/content/103/23/8577.full.pdf+html
[17]
Martin Rosvall and Carl T. Bergstrom. 2008. Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences 105, 4 (2008), 1118--1123. arXiv:http://www.pnas.org/content/105/4/1118.full.pdf+html
[18]
Yizhou Sun, Yintao Yu, and Jiawei Han. 2009. Ranking-based Clustering of Heterogeneous Information Networks with Star Network Schema. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '09). ACM, New York, NY, USA, 797--806.

Cited By

View all
  • (2020)Block models for generalized multipartite networks: Applications in ecology and ethnobiologyStatistical Modelling10.1177/1471082X2096325422:4(273-296)Online publication date: 18-Dec-2020

Index Terms

  1. Community structure detection in multipartite networks: a new fitness measure

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference
    July 2017
    1427 pages
    ISBN:9781450349208
    DOI:10.1145/3071178
    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]

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 01 July 2017

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. community structure
    2. fitness function
    3. multipartite networks

    Qualifiers

    • Research-article

    Funding Sources

    • Romanian National Authority for Scientific Research and Innovation, CNCS - UEFISCDI

    Conference

    GECCO '17
    Sponsor:

    Acceptance Rates

    GECCO '17 Paper Acceptance Rate 178 of 462 submissions, 39%;
    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)8
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 14 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2020)Block models for generalized multipartite networks: Applications in ecology and ethnobiologyStatistical Modelling10.1177/1471082X2096325422:4(273-296)Online publication date: 18-Dec-2020

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media