Skip to main content
SpringerLink
Log in

A comprehensive view of community detection approaches in multilayer social networks

  • Review Paper
  • Published:
Social Network Analysis and Mining Aims and scope Submit manuscript

Abstract

Multilayer social networks are the main representative form for today’s social networks. In fact, the multiplicity of relations, the huge amount of data and the dynamic nature of nowadays social networks impose the representation of the network with multiple layers. This new representation makes network analysis more challenging especially Community retrieval. So, researchers propose different approaches to handle these challenges to detect accurate communities in the multilayer networks. The main goal of this paper is to present a novel and comprehensive view on community detection strategies within multilayer social networks. To do so, we provide a taxonomy of existing methods in static and dynamic multilayer social networks. Additionally, we introduce a "four worlds framework" to offer a comprehensive comparison of the different existing community detection methods. Lastly, we outline potential avenues for future research and highlight some unresolved challenges in this domain.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Notes

  1. In graph theory, there is also a notion of a ‘graph of graphs’ (Lovász 2012).

References

  • Alimadadi F, Khadangi E, Bagheri A (2019) Community detection in Facebook activity networks and presenting a new multilayer label propagation algorithm for community detection. Int J Mod Phys B 33(10):1950089

    Article  Google Scholar 

  • Al-Sharoa E, Al-khassaweneh M, Aviyente S (2018) Temporal block spectral clustering for multi-layer temporal functional connectivity networks. In: 2018 IEEE statistical signal processing workshop (SSP). IEEE, pp 503–507

  • Amelio A, Pizzuti C (2017) Evolutionary clustering for mining and tracking dynamic multilayer networks. Comput Intell 33(2):181–209

    Article  MathSciNet  Google Scholar 

  • Amini A, Paez M, Lin L (2024) Hierarchical stochastic block model for community detection in multiplex networks. Bayesian Anal 19(1):319–345

    Article  MathSciNet  Google Scholar 

  • Azaouzi M, Rhouma D, Ben Romdhane L (2019) Community detection in large-scale social networks: state-of-the-art and future directions. Soc Netw Anal Min 9:1–32

    Article  Google Scholar 

  • Bazzi M, Porter MA, Williams S, McDonald M, Fenn DJ, Howison SD (2016) Community detection in temporal multilayer networks, with an application to correlation networks. Multiscale Model Simul 14(1):1–41

    Article  MathSciNet  Google Scholar 

  • Boccaletti S, Bianconi G, Criado R, Del Genio CI, Gómez-Gardenes J, Romance M, Sendina-Nadal I, Wang Z, Zanin M (2014) The structure and dynamics of multilayer networks. Phys Rep 544(1):1–122

    Article  MathSciNet  Google Scholar 

  • Cazabet R, Chawuthai R, Takeda H (2015) Using multiple-criteria methods to evaluate community partitions. arXiv preprint arXiv:1502.05149

  • Chakraborty T, Dalmia A, Mukherjee A, Ganguly N (2017) Metrics for community analysis: a survey. ACM Comput Surv (CSUR) 50(4):1–37

    Article  Google Scholar 

  • Contisciani M, Power EA, De Bacco C (2020) Community detection with node attributes in multilayer networks. Sci Rep 10(1):15736

    Article  Google Scholar 

  • D’Agostino G, Scala A (2014) Networks of networks: the last Frontier of complexity, vol 340. Springer, Berlin

    Book  Google Scholar 

  • Farzad B, Pichugina O, Koliechkina L (2018) Multi-layer community detection. In: 2018 international conference on control, artificial intelligence, robotics & optimization (ICCAIRO). IEEE, pp 133–140

  • Ford DA, Kaufman JH, Mesika Y (2010) Modeling in space and time: a framework for visualization and collaboration. In: Infectious disease informatics and biosurveillance: research, systems and case studies. Springer, pp 191–206

  • Gamgne Domgue F, Tsopzé N, Ndoundam R (2021) Correlation and dimension relevance in multidimensional networks: a systematic taxonomy. Soc Netw Anal Min 11:1–19

    Article  Google Scholar 

  • Gao J, Buldyrev SV, Havlin S, Stanley HE (2011) Robustness of a network of networks. Phys Rev Lett 107(19):195701

    Article  Google Scholar 

  • Gao X, Zheng Q, Verri FA, Rodrigues RD, Zhao L (2019) Particle competition for multilayer network community detection. In: Proceedings of the 2019 11th international conference on machine learning and computing, pp 75–80

  • Guo X, Li X, Chang X, Ma S (2023) Privacy-preserving community detection for locally distributed multiple networks. arXiv preprint arXiv:2306.15709

  • Hammoud Z, Kramer F (2020) Multilayer networks: aspects, implementations, and application in biomedicine. Big Data Anal 5(1):2

    Article  Google Scholar 

  • He M, Lu D, Xu J, Xavier RM (2021) Community detection in weighted multilayer networks with ambient noise. arXiv preprint arXiv:2103.00486

  • Huang X, Chen D, Ren T, Wang D (2021) A survey of community detection methods in multilayer networks. Data Min Knowl Disc 35:1–45

    Article  MathSciNet  Google Scholar 

  • Huergo RS, Pires PF, Delicato FC, Costa B, Cavalcante E, Batista T (2014) A systematic survey of service identification methods. SOCA 8:199–219

    Article  Google Scholar 

  • Interdonato R, Tagarelli A, Ienco D, Sallaberry A, Poncelet P (2017) Local community detection in multilayer networks. Data Min Knowl Disc 31:1444–1479

    Article  MathSciNet  Google Scholar 

  • Jarke M, Mylopoulos J, Schmidt JW, Vassiliou Y (1992) Daida: an environment for evolving information systems. ACM Trans Inf Syst (TOIS) 10(1):1–50

    Article  Google Scholar 

  • Jia T, Cai C, Li X, Luo X, Zhang Y, Yu X (2022) Dynamical community detection and spatiotemporal analysis in multilayer spatial interaction networks using trajectory data. Int J Geogr Inf Sci 36(9):1719–1740

    Article  Google Scholar 

  • Jin D, Ge M, Li Z, Lu W, He D, Fogelman-Soulie F (2017) Using deep learning for community discovery in social networks. In: 2017 IEEE 29th international conference on tools with artificial intelligence (ICTAI). IEEE, pp 160–167

  • Khawaja FR, Sheng J, Wang B, Memon Y (2021) Uncovering hidden community structure in multi-layer networks. Appl Sci 11(6):2857

    Article  Google Scholar 

  • Kim J, Lee J-G (2015) Community detection in multi-layer graphs: a survey. ACM SIGMOD Rec 44(3):37–48

    Article  Google Scholar 

  • Kivelä M, Arenas A, Barthelemy M, Gleeson JP, Moreno Y, Porter MA (2014) Multilayer networks. J Complex Netw 2(3):203–271

    Article  Google Scholar 

  • Kuncheva Z, Montana G (2015) Community detection in multiplex networks using locally adaptive random walks. In: Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining 2015, pp 1308–1315

  • Lei J, Chen K, Lynch B (2020) Consistent community detection in multi-layer network data. Biometrika 107(1):61–73

    Article  MathSciNet  Google Scholar 

  • Logan AP, LaCasse PM, Lunday BJ (2023) Social network analysis of twitter interactions: a directed multilayer network approach. Soc Netw Anal Min 13(1):65

    Article  Google Scholar 

  • Lovász L (2012) Large networks and graph limits, vol 60. American Mathematical Society, Providence

    Google Scholar 

  • Ma Z, Nandy S (2023) Community detection with contextual multilayer networks. IEEE Trans Inf Theory 69(5):3203–3239

    Article  MathSciNet  Google Scholar 

  • Ma X, Dong D, Wang Q (2018) Community detection in multi-layer networks using joint nonnegative matrix factorization. IEEE Trans Knowl Data Eng 31(2):273–286

    Article  Google Scholar 

  • Magnani M, Hanteer O, Interdonato R, Rossi L, Tagarelli A (2021) Community detection in multiplex networks. ACM Comput Surv (CSUR) 54(3):1–35

    Article  Google Scholar 

  • Newman ME, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69(2):026113

    Article  Google Scholar 

  • Ni L, Ye R, Luo W, Zhang Y (2024) Local community detection in multiple private networks. ACM Trans Knowl Discov Data 18:1–21

    Google Scholar 

  • Ortiz-Bouza M, Aviyente S (2024) Community detection in multiplex networks based on orthogonal nonnegative matrix tri-factorization. IEEE Access 12:6423–6436

    Article  Google Scholar 

  • Paul S, Chen Y (2021) Null models and community detection in multi-layer networks. Sankhya A. https://doi.org/10.1007/s13171-021-00257-0

    Article  Google Scholar 

  • Pizzuti C, Socievole A (2017) Many-objective optimization for community detection in multi-layer networks. In: 2017 IEEE congress on evolutionary computation (CEC). IEEE, pp 411–418

  • Polishchuk O (2023) Communities detection in complex network and multilayer network systems: a flow approach. arXiv preprint arXiv:2309.11418

  • Puxeddu MG, Petti M, Astolfi L (2021) A comprehensive analysis of multilayer community detection algorithms for application to EEG-based brain networks. Front Syst Neurosci 15:624183

    Article  Google Scholar 

  • Rebhi W, Yahia NB, Saoud NBB (2016) Hybrid community detection approach in multilayer social network: scientific collaboration recommendation case study. In: 2016 IEEE/ACS 13th international conference of computer systems and applications (AICCSA). IEEE, pp 1–8

  • Rebhi W, Yahia NB, Saoud NBB (2017) Hybrid modeling approach for contextualized community detection in multilayer social network: emergency management case study. Procedia Comput Sci 112:673–682

    Article  Google Scholar 

  • Rebhi W, Yahia NB, Saoud NBB (2018) Discovering stable communities in dynamic multilayer social networks. In: 2018 IEEE 27th international conference on enabling technologies: infrastructure for collaborative enterprises (WETICE). IEEE, pp 142–147

  • Rebhi W, Ben Yahia N, Bellamine N (2022) Lifelong and multirelational community detection to support social and collaborative e-learning. Comput Appl Eng Educ 30(5):1321–1337

    Article  Google Scholar 

  • Reittu H, Leskelä L, Räty T (2023) A network community detection method with integration of data from multiple layers and node attributes. Netw Sci 11(3):374–396

    Article  Google Scholar 

  • Rossetti G, Cazabet R (2018) Community discovery in dynamic networks: a survey. ACM Comput Surv (CSUR) 51(2):1–37

    Article  Google Scholar 

  • Santra A, Irany FA, Madduri K, Chakravarthy S, Bhowmick S (2023) Efficient community detection in multilayer networks using Boolean compositions. Front Big Data 6:1144793

    Article  Google Scholar 

  • Su X, Xue S, Liu F, Wu J, Yang J, Zhou C, Hu W, Paris C, Nepal S, Jin D et al (2022) A comprehensive survey on community detection with deep learning. IEEE Trans Neural Netw Learn Syst 35:4682–4702

    Article  Google Scholar 

  • Sulisworo D (2023) Exploring research idea growth with litmap: visualizing literature review graphically. Bincang Sains dan Teknologi 2(02):48–54

    Article  Google Scholar 

  • Tagarelli A, Amelio A, Gullo F (2017) Ensemble-based community detection in multilayer networks. Data Min Knowl Disc 31:1506–1543

    Article  MathSciNet  Google Scholar 

  • Venturini S, Cristofari A, Rinaldi F, Tudisco F (2022) A variance-aware multiobjective Louvain-like method for community detection in multiplex networks. J Complex Netw 10(6):048

    MathSciNet  Google Scholar 

  • Wilson JD, Palowitch J, Bhamidi S, Nobel AB (2017) Community extraction in multilayer networks with heterogeneous community structure. J Mach Learn Res 18(1):5458–5506

    MathSciNet  Google Scholar 

  • Yuan Y, Qu A (2021) Community detection with dependent connectivity. Ann Stat 49(4):2378–2428

    Article  MathSciNet  Google Scholar 

  • Zhang J, Wang F, Zhou J (2024) Community detection based on nonnegative matrix tri-factorization for multiplex social networks. J Complex Netw 12(2):012

    MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. RIADI Laboratory, National School of Computer Science, University of Manouba, Manouba, Tunisia

    Imen Hamed, Wala Rebhi & Narjes Bellamine Ben Saoud

Authors
  1. Imen Hamed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wala Rebhi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Narjes Bellamine Ben Saoud
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Imen Hamed.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hamed, I., Rebhi, W. & Saoud, N.B.B. A comprehensive view of community detection approaches in multilayer social networks. Soc. Netw. Anal. Min. 14, 103 (2024). https://doi.org/10.1007/s13278-024-01266-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13278-024-01266-1

Keywords

Search

Navigation