skip to main content
research-article

Community Detection in Multiplex Networks

Published:08 May 2021Publication History
Skip Abstract Section

Abstract

A multiplex network models different modes of interaction among same-type entities. In this article, we provide a taxonomy of community detection algorithms in multiplex networks. We characterize the different algorithms based on various properties and we discuss the type of communities detected by each method. We then provide an extensive experimental evaluation of the reviewed methods to answer three main questions: to what extent the evaluated methods are able to detect ground-truth communities, to what extent different methods produce similar community structures, and to what extent the evaluated methods are scalable. One goal of this survey is to help scholars and practitioners to choose the right methods for the data and the task at hand, while also emphasizing when such choice is problematic.

Skip Supplemental Material Section

Supplemental Material

References

  1. Nazanin Afsarmanesh and Matteo Magnani. 2018. Finding overlapping communities in multiplex networks. In Proceedings of the International Conference on Social Informatics (SocInfo’18).Google ScholarGoogle Scholar
  2. Michael J. Barber. 2007. Modularity and community detection in bipartite networks. Phys. Rev. E 76 (2007), 066102.Google ScholarGoogle ScholarCross RefCross Ref
  3. Marya Bazzi, Lucas G. S. Jeub, Alex Arenas, Sam D. Howison, and Mason A. Porter. 2020. A framework for the construction of generative models for mesoscale structure in multilayer networks. Phys. Rev. Res. 2, 4 (2020), 023100. Retrieved from https://link.aps.org/doi/10.1103/PhysRevResearch.2.023100.Google ScholarGoogle ScholarCross RefCross Ref
  4. Michele Berlingerio, Michele Coscia, and Fosca Giannotti. 2011. Finding and characterizing communities in multidimensional networks. In Proceedings of the International Conference on Advances in Social Networks Analysis and Mining (ASONAM’11). IEEE Computer Society Washington, DC, 490--494.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Michele Berlingerio, Fabio Pinelli, and Francesco Calabrese. 2013. ABACUS: Frequent pAttern mining-BAsed Community discovery in mUltidimensional networkS. Data Min. Knowl. Discov. 27, 3 (2013), 294--320. arxiv:arXiv:1303.2025v2Google ScholarGoogle ScholarCross RefCross Ref
  6. Brigitte Boden, Stephan Günnemann, Holger Hoffmann, and Thomas Seidl. 2012. Mining coherent subgraphs in multi-layer graphs with edge labels. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’12). ACM Press, 1258. DOI:https://doi.org/10.1145/2339530.2339726Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Cecile Bothorel, Juan David Cruz, Matteo Magnani, and Barbora Micenkova. 2015. Clustering attributed graphs: Models, measures and methods. Netw. Sci. 3, 3 (2015), 408--444.Google ScholarGoogle ScholarCross RefCross Ref
  8. Oualid Boutemine and Mohamed Bouguessa. 2017. Mining community structures in multidimensional networks. ACM Trans. Knowl. Discov. Data 11, 4 (June 2017), 1--36. DOI:https://doi.org/10.1145/3080574Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Piotr Bródka. 2016. A method for group extraction and analysis in multilayer social networks. CoRR, abs/1612.02377. https://arxiv.org/abs/1612.023775.Google ScholarGoogle Scholar
  10. Piotr Bródka, Anna Chmiel, Matteo Magnani, and Giancarlo Ragozini. 2018. Quantifying layer similarity in multiplex networks: A systematic study. Roy. Soc. Open Sci. (2018). DOI:https://doi.org/10.1098/rsos.171747 Retrieved from https://arxiv:1711.11335.Google ScholarGoogle Scholar
  11. Piotr Bródka, Tomasz Filipowski, and Przemysław Kazienko. 2013. An introduction to community detection in multi-layered social network. In Information Systems, E-learning, and Knowledge Management Research. Springer, Berlin, 185--190. DOI:https://doi.org/10.1007/978-3-642-35879-1_23Google ScholarGoogle Scholar
  12. Piotr Bródka, Krzysztof Skibicki, Przemyslaw Kazienko, and Katarzyna Musial. 2011. A degree centrality in multi-layered social network. In Proceedings of the International Conference on Computational Aspects of Social Networks (CASoN’11). IEEE, 237--242. DOI:https://doi.org/10.1109/CASON.2011.6085951 Retrieved from https://arxiv:1210.5184.Google ScholarGoogle ScholarCross RefCross Ref
  13. Alessio Cardillo, Jesús Gómez-Gardeñes, Massimiliano Zanin, Miguel Romance, David Papo, Francisco del Pozo, and Stefano Boccaletti. 2013. Emergence of network features from multiplexity. Sci. Rep. 3, 1344 (2013). https://doi.org/10.1038/srep01344Google ScholarGoogle Scholar
  14. Jiyang Chen, Osmar R. Zaïane, and Randy Goebel. 2009. Local community identification in social networks. In Proceedings of the International Conference on Advances in Social Network Analysis and Mining (ASONAM’09). 237--242.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Linda M. Collins and Clyde W. Dent. 1988. Omega: A general formulation of the rand index of cluster recovery suitable for non-disjoint solutions. Multivar. Behav. Res. 23, 2 (Apr. 1988), 231--242.Google ScholarGoogle ScholarCross RefCross Ref
  16. Manlio De Domenico, Andrea Lancichinetti, Alex Arenas, and Martin Rosvall. 2015. Identifying modular flows on multilayer networks reveals highly overlapping organization in interconnected systems. Phys. Rev. X 5 (Mar. 2015), 011027. Issue 1. DOI:https://doi.org/10.1103/PhysRevX.5.011027Google ScholarGoogle Scholar
  17. Manlio De Domenico, Vincenzo Nicosia, Alexandre Arenas, and Vito Latora. 2015. ARTICLE structural reducibility of multilayer networks. Nature Commun. 6, 6864 (2015). https://doi.org/10.1038/ncomms7864Google ScholarGoogle Scholar
  18. Mark E. Dickison, Matteo Magnani, and Luca Rossi. 2016. Multilayer Social Networks. Cambridge University Press.Google ScholarGoogle Scholar
  19. Xiaowen Dong, Pascal Frossard, Pierre Vandergheynst, and Nikolai Nefedov. 2014. Clustering on multi-layer graphs via subspace analysis on grassmann manifolds. IEEE Trans. Signal Process. 62, 4 (Feb. 2014), 905--918. DOI:https://doi.org/10.1109/TSP.2013.2295553Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Daniel Edler, Ludvig Bohlin, and Martin Rosvall. 2017. Mapping higher-order network flows in memory and multilayer networks with Infomap. Retrieved from https://arxiv:1706.04792.Google ScholarGoogle Scholar
  21. Amir Ghasemian, Homa Hosseinmardi, and Aaron Clauset. 2019. Evaluating overfit and underfit in models of network community structure. IEEE Trans. Knowl. Data Eng. 32, 9 (2019), 1722--1735. DOI:https://doi.org/10.1109/TKDE.2019.2911585Google ScholarGoogle Scholar
  22. Roger Guimera, Marta Sales-Pardo, and Luis A. Nunes Amaral. 2007. Module identification in bipartite and directed networks. Phys. Rev. E 76 (2007), 036102.Google ScholarGoogle ScholarCross RefCross Ref
  23. Obaida Hanteer and Matteo Magnani. 2020. Unspoken assumptions in multi-layer modularity maximization. Sc. Rep. 10, 1 (2020), 11053. DOI:https://doi.org/10.1038/s41598-020-66956-0Google ScholarGoogle ScholarCross RefCross Ref
  24. Obaida Hanteer and Luca Rossi. 2019. The meaning of dissimilar: An evaluation of various similarity quantification approaches used to evaluate community detection solutions. In Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. 513--518.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Obaida Hanteer, Luca Rossi, Davide Vega D’Aurelio, and Matteo Magnani. 2018. From interaction to participation: The role of the imagined audience in social media community detection and an application to political communication on Twitter. In Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM, Ulrik Brandes, Chandan Reddy, and Andrea Tagarelli (Eds.). IEEE Computer Society, 531--534. DOI:https://doi.org/10.1109/ASONAM.2018.8508575Google ScholarGoogle Scholar
  26. Roberto Interdonato, Matteo Magnani, Diego Perna, Andrea Tagarelli, and Davide Vega. 2020. Multilayer network simplification: Approaches, models and methods. Comput. Sci. Rev. 36 (2020), 100246. DOI:https://doi.org/10.1016/j.cosrev.2020.100246Google ScholarGoogle ScholarCross RefCross Ref
  27. Roberto Interdonato, Andrea Tagarelli, Dino Ienco, Arnaud Sallaberry, and Pascal Poncelet. 2017. Node-centric community detection in multilayer networks with layer-coverage diversification bias. In Proceedings of the 8th Conference on Complex Networks (CompleNet’17). Springer International Publishing, 57--66.Google ScholarGoogle ScholarCross RefCross Ref
  28. Lucas G. S. Jeub, Michael W. Mahoney, Peter J. Mucha, and Mason A. Porter. 2017. A local perspective on community structure in multilayer networks. Netw. Sci. 5, 2 (2017), 144--163. DOI:https://doi.org/10.1017/nws.2016.22Google ScholarGoogle ScholarCross RefCross Ref
  29. Inderjit S. Jutla, Lucas G. S. Jeub, and Peter J. Mucha. 2011-2017. A Generalized Louvain Method for Community Detection Implemented in Matlab. Technical Report. Retrieved from http://github.com/GenLouvain.Google ScholarGoogle Scholar
  30. Jungeun Kim and Jae-Gil Lee. 2015. Community detection in multi-layer graphs. ACM SIGMOD Rec. 44, 3 (2015), 37--48.Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Jungeun Kim, Jae-gil Lee, and Sungsu Lim. 2016. Differential flattening: A novel framework for community detection in multi-layer graphs. ACM Trans. Intell. Syst. Technol. 8, 2 (2016), 27.Google ScholarGoogle Scholar
  32. Mikko Kivelä, Alexandre Arenas, Marc Barthelemy, James P. Gleeson, Yamir Moreno, and Mason A. Porter. 2014. Multilayer networks. J. Complex Netw. 2, 3 (Sep. 2014), 203--271. DOI:https://doi.org/doi:10.1093/comnet/cnu016Google ScholarGoogle ScholarCross RefCross Ref
  33. Zhana Kuncheva and Giovanni Montana. 2015. Community detection in multiplex networks using locally adaptive random walks. In Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM. ACM Press, 1308--1315. DOI:https://doi.org/10.1145/2808797.2808852Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Andrea Lancichinetti and Santo Fortunato. 2012. Consensus clustering in complex networks. Sci. Rep. 2, 336 (2012).Google ScholarGoogle Scholar
  35. Huajing Li, Zaiqing Nie, Wang-Chien Lee, Lee Giles, and Ji-Rong Wen. 2008. Scalable community discovery on textual data with relations. In Proceedings of the 17th ACM Conference on Information and Knowledge Management (CIKM’08). ACM, New York, NY, 1203--1212. DOI:https://doi.org/10.1145/1458082.1458241Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Yixuan Li, Kun He, David Bindel, and John E. Hopcroft. 2015. Uncovering the small community structure in large networks: A local spectral approach. In Proceedings of the 24th International Conference on World Wide Web (WWW’15). 658--668.Google ScholarGoogle Scholar
  37. Chuan Wen Loe and Henrik Jeldtoft Jensen. 2015. Comparison of communities detection algorithms for multiplex. Physica A 431 (2015), 29--45. DOI:https://doi.org/10.1016/j.physa.2015.02.089 Retrieved from https://arxiv:arXiv:1406.2205v1.Google ScholarGoogle ScholarCross RefCross Ref
  38. Lijia Ma, Maoguo Gong, Jianan Yan, Wenfeng Liu, and Shanfeng Wang. 2018. Detecting composite communities in multiplex networks: A multilevel memetic algorithm. Swarm Evolution. Comput. 39 (Apr. 2018), 177--191. DOI:https://doi.org/10.1016/J.SWEVO.2017.09.012Google ScholarGoogle Scholar
  39. Matteo Magnani and Luca Rossi. 2011. The ML-model for multi-layer social networks. In Proceedings of the International Conference on Advances in Social Networks Analysis and Mining (ASONAM’11). DOI:https://doi.org/10.1109/ASONAM.2011.114Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Matteo Magnani and Luca Rossi. 2013. Formation of multiple networks. In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).Google ScholarGoogle Scholar
  41. Raul J. Mondragon, Jacopo Iacovacci, and Ginestra Bianconi. 2018. Multilink communities of multiplex networks. PLOS One 13, 3 (Mar. 2018), e0193821. DOI:https://doi.org/10.1371/journal.pone.0193821Google ScholarGoogle ScholarCross RefCross Ref
  42. Peter J. Mucha and Mason A. Porter. 2010. Communities in multislice voting networks. Chaos 20, 4 (2010). DOI:https://doi.org/10.1063/1.3518696Google ScholarGoogle Scholar
  43. Peter J. Mucha, Thomas Richardson, Kevin Macon, Mason A. Porter, and Jukka-Pekka Onnela. 2010. Community structure in time-dependent, multiscale, and multiplex networks.Science 328, 5980 (May 2010), 876--878. DOI:https://doi.org/10.1126/science.1184819Google ScholarGoogle Scholar
  44. Gabriel Murray, Giuseppe Carenini, and Raymond Ng. 2012. Using the omega index for evaluating abstractive community detection. In Proceedings of Workshop on Evaluation Metrics and System Comparison for Automatic Summarization. Association for Computational Linguistics, 10--18.Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Vincenzo Nicosia, Ginestra Bianconi, Vito Latora, and Marc Barthelemy. 2013. Growing multiplex networks. Phys. Rev. Lett. 111 (2013), 058701. Retrieved from http://prl.aps.org/abstract/PRL/v111/i5/e058701.Google ScholarGoogle ScholarCross RefCross Ref
  46. Leto Peel, Daniel B. Larremore, and Aaron Clauset. 2017. The ground truth about metadata and community detection in networks. Sci. Adv. 3, 5 (May 2017), e1602548. DOI:https://doi.org/10.1126/sciadv.1602548Google ScholarGoogle Scholar
  47. Clara Pizzuti and Annalisa Socievole. 2017. Many-objective optimization for community detection in multi-layer networks. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC’17). IEEE, 411--418. DOI:https://doi.org/10.1109/CEC.2017.7969341Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Guo-Jun Qi, Charu C. Aggarwal, and Thomas Huang. 2012. Community detection with edge content in social media networks. In Proceedings of the IEEE 28th International Conference on Data Engineering (ICDE’12), Vol. 00. 534--545. DOI:https://doi.org/10.1109/ICDE.2012.77Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. Luca Rossi and Matteo Magnani. 2015. Towards effective visual analytics on multiplex and multilayer networks. Chaos, Solitons Fractals 72 (2015). DOI:https://doi.org/10.1016/j.chaos.2014.12.022Google ScholarGoogle Scholar
  50. Yiye Ruan, David Fuhry, and Srinivasan Parthasarathy. 2012. Efficient community detection in large networks using content and links. https://arxiv.org/abs/1212.0146.Google ScholarGoogle Scholar
  51. Arlei Silva, Wagner Meira, Jr., and Mohammed J. Zaki. 2012. Mining attribute-structure correlated patterns in large attributed graphs. Proc. VLDB Endow. 5, 5 (Jan. 2012), 466--477. DOI:https://doi.org/10.14778/2140436.2140443Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. Chris Stark, Bobby-Joe Breitkreutz, Teresa Reguly, Lorrie Boucher, Ashton Breitkreutz, and Mike Tyers. 2006. BioGRID: A general repository for interaction datasets. Nucleic Acids Res. 34 (2006), D535--D539.Google ScholarGoogle ScholarCross RefCross Ref
  53. Yizhou Sun and Jiawei Han. 2013. Mining heterogeneous information networks: A structural analysis approach. ACM SIGKDD Explor. Newslett. 14, 2 (2013), 20--28.Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Yizhou Sun, Jiawei Han, Peixiang Zhao, Zhijun Yin, Hong Cheng, and Tianyi Wu. 2009. RankClus: Integrating clustering with ranking for heterogeneous information network analysis. In Proceedings of the International Conference on Extending Database Technology (EDBT’09). ACM Press, 565--576. DOI:https://doi.org/10.1145/1516360.1516426Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. 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 Press, 797. DOI:https://doi.org/10.1145/1557019.1557107Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. Andrea Tagarelli, Alessia Amelio, and Francesco Gullo. 2017. Ensemble-based community detection in multilayer networks. Data Min. Knowl. Discov. 31, 5 (Sep. 2017), 1506--1543. DOI:https://doi.org/10.1007/s10618-017-0528-8Google ScholarGoogle ScholarDigital LibraryDigital Library
  57. Lei Tang and Huan Liu. 2010. Community Detection and Mining in Social Media. Morgan & Claypool Publishers.Google ScholarGoogle Scholar
  58. Lei Tang, Xufei Wang, and Huan Liu. 2009. Uncoverning groups via heterogeneous interaction analysis. In Proceedings of the 9th IEEE International Conference on Data Mining. 503--512.Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. Lei Tang, Xufei Wang, and Huan Liu. 2012. Community detection via heterogeneous interaction analysis. Data Min. Knowl. Discov. 25, 1 (July 2012), 1--33. DOI:https://doi.org/10.1007/s10618-011-0231-0Google ScholarGoogle ScholarDigital LibraryDigital Library
  60. Zhiqiang Xu, Yiping Ke, Yi Wang, Hong Cheng, and James Cheng. 2012. A model-based approach to attributed graph clustering. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD’12). ACM, New York, NY, 505--516. DOI:https://doi.org/10.1145/2213836.2213894Google ScholarGoogle ScholarDigital LibraryDigital Library
  61. Xuemeng Zhai, Wanlei Zhou, Gaolei Fei, Weiyi Liu, Zhoujun Xu, Chengbo Jiao, Cai Lu, and Guangmin Hu. 2018. Null model and community structure in multiplex networks. Sci. Rep. 8, 1 (Dec. 2018), 3245. DOI:https://doi.org/10.1038/s41598-018-21286-0Google ScholarGoogle Scholar
  62. Yang Zhou and Ling Liu. 2013. Social influence based clustering of heterogeneous information networks. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 338. DOI:https://doi.org/10.1145/2487575.2487640Google ScholarGoogle ScholarDigital LibraryDigital Library
  63. Yang Zhou, Hong Cheng, and Jeffrey Xu Yu. 2009. Graph clustering based on structural/attribute similarities. Proc. VLDB Endow. 2, 1 (2009), 718--729. DOI:https://doi.org/10.14778/1687627.1687709Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Community Detection in Multiplex Networks

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in

      Full Access

      • Published in

        cover image ACM Computing Surveys
        ACM Computing Surveys  Volume 54, Issue 3
        April 2022
        836 pages
        ISSN:0360-0300
        EISSN:1557-7341
        DOI:10.1145/3461619
        Issue’s Table of Contents

        Copyright © 2021 ACM

        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: 8 May 2021
        • Revised: 1 December 2020
        • Accepted: 1 December 2020
        • Received: 1 March 2019
        Published in csur Volume 54, Issue 3

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article
        • Research
        • Refereed

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      HTML Format

      View this article in HTML Format .

      View HTML Format