Skip to main content
SpringerLink
Log in

Genetic Algorithm with a Novel Leiden-based Mutation Operator for Community Detection

  • Conference paper
  • First Online:
AI 2022: Advances in Artificial Intelligence (AI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13728))

Included in the following conference series:

Abstract

Detecting quality community structures in complex networks is an important and highly active research area. Plenty of methods have been proposed for community detection in recent years. Among them, Genetic Algorithms (GAs) have been widely explored for community detection due to their strong competence at exploring the global discrete search space. However, existing GA algorithms for community detection still face major challenges when handling large and complex networks due to their use of random mutation operators. Whenever any candidate community structure in a GA population is mutated, a mutated node of the network under processing is often associated to a community with loose connections, seriously hurting GA’s effectiveness and scalability. To address this issue, a newly designed Leiden-based GA (LGA) with a novel mutation operator based on the Leiden algorithm is proposed in this paper to improve the effectiveness of the mutation operator and the performance of the GA approach. Experiment results clearly show that LGA can achieve highly competitive performance in comparison to several state-of-the-art GA and non-GA community detection algorithms on multiple synthetic and real-world networks.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Arasteh, M., Alizadeh, S.: A fast divisive community detection algorithm based on edge degree betweenness centrality. Appl. Intell. 49(2), 689–702 (2019)

    Article  Google Scholar 

  2. Barabasi, A.-L., Oltvai, Z.N.: Network biology: understanding the cell’s functional organization. Nat. Rev. Genetics 5(2), 101–113 (2004)

    Article  Google Scholar 

  3. Barber, M.J., Clark, J.W.: Detecting network communities by propagating labels under constraints. Phys. Rev. E 80, 2 (2009)

    Article  Google Scholar 

  4. Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Statistic. Mech. Theory Exper. 2008, 10 (2008)

    MATH  Google Scholar 

  5. Dhilber, M., Bhavani, S.D.: Community detection in social networks using deep learning. In: Hung, D., Van D’Souza, M. (eds.) ICDCIT 2020. LNCS, vol. 11969, pp. 241–250. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-36987-3_15

  6. Ebrahimi, M., Shahmoradi, M.R., Heshmati, Z., Salehi, M.: A novel method for overlapping community detection using multi-objective optimization. Phys. A Statist. Mech. Appl. 505, 825–835 (2018)

    Article  Google Scholar 

  7. Guimera, R., Mossa, S., Turtschi, A., Amaral, L.N.: The worldwide air transportation network: anomalous centrality, community structure, and cities’ global roles. In: Proceedings of the National Academy of Sciences, pp. 7794–7799. National Academic Sciences (2005)

    Google Scholar 

  8. Guo, X., Su, J., Zhou, H., Liu, C., Cao, J., Li, L.: Community detection based on genetic algorithm using local structural similarity. IEEE Access 7, 134583–134600 (2019)

    Article  Google Scholar 

  9. Hosseini, R., Rezvanian, A.: Antlp: ant-based label propagation algorithm for community detection in social networks. CAAI Trans. Intell. Technol. 5(1), 34–41 (2020)

    Article  Google Scholar 

  10. Jin, D., Ge, M., Li, Z., Lu, W., He, D., Fogelman-Soulie, F.: Using deep learning for community discovery in social networks. In: 2017 IEEE 29th International Conference on Tools with Artificial Intelligence, pp. 160–167. IEEE (2017)

    Google Scholar 

  11. Kong, H., Kang, Q., Li, W., Liu, C., Kang, Y., He, H.: A hybrid iterated carousel greedy algorithm for community detection in complex networks. Phys. A Statist. Mech. Appl. 536 (2019)

    Google Scholar 

  12. Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Phys. Rev. E 78, 4 (2008)

    Article  Google Scholar 

  13. Leskovec, J., Mcauley, J.: Learning to discover social circles in ego networks. Adv. Neural Inform. Process. Syst. 25 (2012)

    Google Scholar 

  14. Liu, F., et al.: Deep learning for community detection: progress, challenges and opportunities. arXiv preprint arXiv:2005.08225 (2020)

  15. Liu, Z., Sun, Y., Cheng, S., Sun, X., Bian, K., Yao, R.: A node influence based memetic algorithm for community detection in complex networks. In: Pan, L., Cui, Z., Cai, J., Li, L. (eds.) BIC-TA 2021. CCIS, vol. 1565, pp. 217–231. Springer, Singapore (2022). https://doi.org/10.1007/978-981-19-1256-6_16

  16. Moradi, M., Parsa, S.: An evolutionary method for community detection using a novel local search strategy. Phys. A Statist. Mech. Appl. 523, 457–475 (2019)

    Article  Google Scholar 

  17. Onnela, J.-P., et al.: Structure and tie strengths in mobile communication networks. In: Proceedings of the National Academy of Sciences, pp. 7332–7336. National Acad Sciences (2007)

    Google Scholar 

  18. Osaba, E., Del Ser, J., Camacho, D., Bilbao, M.N., Yang, X.-S.: Community detection in networks using bio-inspired optimization: latest developments, new results and perspectives with a selection of recent meta-heuristics. Appl. Soft Comput. 87 (2020)

    Google Scholar 

  19. Park, Y., Song, M., et al.: A genetic algorithm for clustering problems. In: Proceedings of the Third Annual Conference on Genetic Programming, vol. 1998, pp. 568–575. Morgan Kaufmann San Francisco (1998)

    Google Scholar 

  20. Pizzuti, C.: GA-Net: a genetic algorithm for community detection in social networks. In: Rudolph, G., Jansen, T., Beume, N., Lucas, S., Poloni, C. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 1081–1090. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-87700-4_107

  21. Pizzuti, C.: A multiobjective genetic algorithm to find communities in complex networks. IEEE Trans. Evol. Comput. 16(3), 418–430 (2011)

    Article  Google Scholar 

  22. Pizzuti, C.: Evolutionary computation for community detection in networks: a review. IEEE Trans. Evol. Comput. 22(3), 464–483 (2017)

    Article  Google Scholar 

  23. Rossi, R.A., Ahmed, N.K.: The network data repository with interactive graph analytics and visualization. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)

    Google Scholar 

  24. Said, A., Abbasi, R.A., Maqbool, O., Daud, A., Aljohani, N.R.: CC-GA: a clustering coefficient based genetic algorithm for detecting communities in social networks. Appl. Soft Comput. 63, 59–70 (2018)

    Article  Google Scholar 

  25. Salwinski, L., Miller, C.S., Smith, A.J., Pettit, F.K., Bowie, J.U., Eisenberg, D.: The database of interacting proteins: 2004 update. Nucleic Acids Res. 32, 449–451 (2004)

    Google Scholar 

  26. Sanchez-Oro, J., Duarte, A.: Iterated greedy algorithm for performing community detection in social networks. Future Generat. Comput. Syst. 88, 785–791 (2018)

    Article  Google Scholar 

  27. Shen-Orr, S.S., Milo, R., Mangan, S., Alon, U.: Network motifs in the transcriptional regulation network of escherichia coli. Nat. Genet. 31(1), 64–68 (2002)

    Article  Google Scholar 

  28. Su, X., et al.: A comprehensive survey on community detection with deep learning. IEEE Trans. Neural Netw. Learn. Syst. (2022)

    Google Scholar 

  29. Sun, X., Sun, Y., Cheng, S., Bian, K., Liu, Z.: Population learning based memetic algorithm for community detection in complex networks. In: Tan, Y., Shi, Y., Zomaya, A., Yan, H., Cai, J. (eds.) DMBD 2021. CCIS, vol. 1454, pp. 275–288. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-7502-7_29

  30. Traag, V.A., Waltman, L., Van Eck, N.J.: From louvain to leiden: guaranteeing well-connected communities. Sci. Reports 9(1), 1–12 (2019)

    Google Scholar 

  31. Yin, H., Benson, A.R., Leskovec, J., Gleich, D.F.: Local higher-order graph clustering. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 555–564 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Victoria University of Wellington, Wellington, New Zealand

    Anjali de Silva, Aaron Chen, Hui Ma & Mohammad Nekooei

Authors
  1. Anjali de Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aaron Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hui Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohammad Nekooei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anjali de Silva .

Editor information

Editors and Affiliations

  1. University of New South Wales, Sydney, NSW, Australia

    Haris Aziz

  2. University of Western Australia, Perth, WA, Australia

    Débora Corrêa

  3. University of Western Australia, Perth, WA, Australia

    Tim French

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

de Silva, A., Chen, A., Ma, H., Nekooei, M. (2022). Genetic Algorithm with a Novel Leiden-based Mutation Operator for Community Detection. In: Aziz, H., Corrêa, D., French, T. (eds) AI 2022: Advances in Artificial Intelligence. AI 2022. Lecture Notes in Computer Science(), vol 13728. Springer, Cham. https://doi.org/10.1007/978-3-031-22695-3_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-22695-3_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-22694-6

  • Online ISBN: 978-3-031-22695-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation