Skip to main content
Springer Nature Link
Log in

Unsupervised Multi-population Evolutionary Algorithm for Community Detection in Attributed Networks

  • Conference paper
  • First Online:
Computer Supported Cooperative Work and Social Computing (ChineseCSCW 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2012))

  • 549 Accesses

Abstract

Community detection on attributed networks is a method to discover community structures within attributed networks. By applying community detection on attribute networks, we can better understand the relationships between nodes in real-world networks. However, current algorithms for community detection on attribute networks rely on hyper-parameters, and it is difficult to obtain an ideal result when facing networks with inconsistent attributes and topology. Consequently, we propose an Unsupervised Multi-population Evolutionary Algorithm (UMEA) for community detection in attributed networks. This algorithm adds edges between nodes based on attribute similarity, allowing it to combine attribute information during the process of community detection. In addition, this algorithm determines the optimal number of added edges autonomously through communication and learning between multiple populations. Furthermore, we propose a series of strategies to accelerate population convergence for the locus-based encoding. Experiments have demonstrated that our algorithm outperforms the benchmark algorithms in both real and artificial networks.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Ma, H., Liu, Z., Zhang, X., Zhang, L., Jiang, H.: Balancing topology structure and node attribute in evolutionary multi-objective community detection for attributed networks. Knowl.-Based Syst. 227, 107169 (2021)

    Article  Google Scholar 

  2. Shi, C., Yan, Z., Cai, Y., Wu, B.: Multi-objective community detection in complex networks. Appl. Soft Comput. 12(2), 850–859 (2012)

    Article  Google Scholar 

  3. Zhang, X., Zhou, K., Pan, H., Zhang, L., Zeng, X., Jin, Y.: A network reduction-based multiobjective evolutionary algorithm for community detection in large-scale complex networks. IEEE Trans. Cybern. 50(2), 703–716 (2018)

    Article  Google Scholar 

  4. He, T., Chan, K.C.: Evolutionary community detection in social networks. In: IEEE Congress on Evolutionary Computation (CEC), vol. 2014, pp. 1496–1503. IEEE (2014)

    Google Scholar 

  5. Sun, J., Zheng, W., Zhang, Q., Xu, Z.: Graph neural network encoding for community detection in attribute networks. IEEE Trans. Cybern. 52(8), 7791–7804 (2021)

    Article  Google Scholar 

  6. Wang, C., Pan, S., Long, G., Zhu, X., Jiang, J.: MGAE: marginalized graph autoencoder for graph clustering. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 889–898 (2017)

    Google Scholar 

  7. Li, Y., Sha, C., Huang, X., Zhang, Y.: Community detection in attributed graphs: an embedding approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1 (2018)

    Google Scholar 

  8. Naderipour, M., Zarandi, M.H.F., Bastani, S.: A fuzzy cluster-validity index based on the topology structure and node attribute in complex networks. Expert Syst. Appl. 187, 115913 (2022)

    Article  Google Scholar 

  9. Kakisim, A.G.: Enhancing attributed network embedding via enriched attribute representations. Appl. Intell. 52(2), 1566–1580 (2022)

    Article  Google Scholar 

  10. Bothorel, C., Cruz, J.D., Magnani, M., Micenkova, B.: Clustering attributed graphs: models, measures and methods. Netw. Sci. 3(3), 408–444 (2015)

    Article  Google Scholar 

  11. Newman, M.E.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577–8582 (2006)

    Article  Google Scholar 

  12. Lu, D.-D., Qi, J., Yan, J., Zhang, Z.-Y.: Community detection combining topology and attribute information. Knowl. Inf. Syst. 64(2), 537–558 (2022)

    Article  Google Scholar 

  13. Amelio, A., Pizzuti, C.: Is normalized mutual information a fair measure for comparing community detection methods? In: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, vol. 2015, pp. 1584–1585 (2015)

    Google Scholar 

  14. Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710 (2014)

    Google Scholar 

  15. Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016)

    Google Scholar 

  16. Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech: Theory Exp. 2008(10), P10008 (2008)

    Article  Google Scholar 

  17. Wang, X., Jin, D., Cao, X., Yang, L., Zhang, W.: Semantic community identification in large attribute networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30, no. 1 (2016)

    Google Scholar 

  18. Sun, F.-Y., Qu, M., Hoffmann, J., Huang, C.-W., Tang, J.: vgraph: a generative model for joint community detection and node representation learning. In: Advances in Neural Information Processing Systems, vol. 32 (2019)

    Google Scholar 

  19. Li, Z., Liu, J., Wu, K.: A multiobjective evolutionary algorithm based on structural and attribute similarities for community detection in attributed networks. IEEE Trans. Cybern. 48(7), 1963–1976 (2017)

    Article  Google Scholar 

Download references

Acknowledgement

This work was supported by the National Natural Science Foundation of China under Grant No. 62002063 and No. U21A20472, the National Key Research and Development Plan of China under Grant No.2021YFB3600503, the Fujian Collaborative Innovation Center for Big Data Applications in Governments, the Fujian Industry-Academy Cooperation Project under Grant No. 2017H6008 and No. 2018H6010, the Natural Science Foundation of Fujian Province under Grant No.2022J01118, No.2020J05112 and No.2020J01420, the Fujian Provincial Department of Education under Grant No.JAT190026, the Major Science and Technology Project of Fujian Province under Grant No.2021HZ022007 and Haixi Government Big Data Application Cooperative Innovation Center.

Author information

Authors and Affiliations

  1. College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China

    Junjie Wu, Lin Wu & Kun Guo

  2. Fujian Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University), Fuzhou, 350108, China

    Junjie Wu, Lin Wu & Kun Guo

  3. Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou, 350108, China

    Lin Wu & Kun Guo

Authors
  1. Junjie Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lin Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kun Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kun Guo .

Editor information

Editors and Affiliations

  1. Shandong University, Jinan, China

    Yuqing Sun

  2. Fudan University, Shanghai, China

    Tun Lu

  3. Harbin Engineering University, Harbin, China

    Tong Wang

  4. Tongji University, Shanghai, China

    Hongfei Fan

  5. Guangdong University of Technology, Guangzhou, China

    Dongning Liu

  6. Tongji University, Shanghai, China

    Bowen Du

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wu, J., Wu, L., Guo, K. (2024). Unsupervised Multi-population Evolutionary Algorithm for Community Detection in Attributed Networks. In: Sun, Y., Lu, T., Wang, T., Fan, H., Liu, D., Du, B. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2023. Communications in Computer and Information Science, vol 2012. Springer, Singapore. https://doi.org/10.1007/978-981-99-9637-7_11

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-9637-7_11

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-9636-0

  • Online ISBN: 978-981-99-9637-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships