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Meta-Heuristic Multi-objective Community Detection Based on Users’ Attributes

  • Alireza Moayedekia
  • Kok-Leong Ong
  • Yee Ling Boo
  • William Yeoh
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 845)

Abstract

Community detection (CD) is the act of grouping similar objects. This has applications in social networks. The conventional CD algorithms focus on finding communities from one single perspective (objective) such as structure. However, reliance on only one objective of structure. This makes the algorithm biased, in the sense that objects are well separated in terms of structure, while weakly separated in terms of other objective function (e.g., attribute). To overcome this issue, novel multi-objective community detection algorithms focus on two objective functions, and try to find a proper balance between these two objective functions. In this paper we use Harmony Search (HS) algorithm and integrate it with Pareto Envelope-Based Selection Algorithm 2 (PESA-II) algorithm to introduce a new multi-objective harmony search based community detection algorithm. The integration of PESA-II and HS helps to identify those non-dominated individuals, and using that individuals during improvisation steps new harmony vectors will be generated. In this paper we experimentally show the performance of the proposed algorithm and compare it against two other multi-objective evolutionary based community detection algorithms, in terms of structure (modularity) and attribute (homogeneity). The experimental results indicate that the proposed algorithm is outperforming or showing comparable performances.

Keywords

Attributed communities Community detection Harmony search 

References

  1. 1.
    Amiri, B., et al.: Community detection in complex networks: multi–objective enhanced firefly algorithm. Knowl.-Based Syst. 46, 1–11 (2013)CrossRefGoogle Scholar
  2. 2.
    Corne, D.W., et al.: PESA-II: Region-based selection in evolutionary multiobjective optimization. In: Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation (2001)Google Scholar
  3. 3.
    Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3), 75–174 (2010)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Geem, Z.W.: Novel derivative of harmony search algorithm for discrete design variables. Appl. Math. Comput. 199(1), 223–230 (2008)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Girvan, M., Newman, M.E.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99(12), 7821–7826 (2002)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Gong, M., et al.: Identification of multi-resolution network structures with multi-objective immune algorithm. Appl. Soft Comput. 13(4), 1705–1717 (2013)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Gong, M., et al.: Community detection in networks by using multiobjective evolutionary algorithm with decomposition. Phys. A: Stat. Mech. Appl. 391(15), 4050–4060 (2012)CrossRefGoogle Scholar
  8. 8.
    Hariz, W.A., Abdulhalim, M.F.: Improving the performance of evolutionary multi-objective co-clustering models for community detection in complex social networks. Swarm Evol. Comput. 26, 137–156 (2016)CrossRefGoogle Scholar
  9. 9.
    Li, S., et al.: Detecting community structure via synchronous label propagation. Neurocomputing 151, 1063–1075 (2015)CrossRefGoogle Scholar
  10. 10.
    Li, T., Ma, S., Ogihara, M.: Entropy-based criterion in categorical clustering. In: Proceedings of the Twenty-First International Conference on Machine Learning (2004)Google Scholar
  11. 11.
    Li, Y., et al.: A spectral clustering-based adaptive hybrid multi-objective harmony search algorithm for community detection. In: IEEE Congress on Evolutionary Computation (CEC) (2012)Google Scholar
  12. 12.
    Moser, F., et al.: Mining cohesive patterns from graphs with feature vectors. In: Proceedings of the SIAM International Conference on Data Mining (2009)CrossRefGoogle Scholar
  13. 13.
    Newman, M.E.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577–8582 (2006)CrossRefGoogle Scholar
  14. 14.
    Newman, M.E., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004)CrossRefGoogle Scholar
  15. 15.
    Pool, S., Bonchi, F., van Leeuwen, M.: Description-driven community detection. ACM Trans. Intell. Syst. Technol. TIST 5(2), 28 (2014)Google Scholar
  16. 16.
    Sese, J., Seki, M., Fukuzaki, M.: Mining networks with shared items. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management (2010)Google Scholar
  17. 17.
    Shi, C., et al.: Multi-objective community detection in complex networks. Appl. Soft Comput. 12(2), 850–859 (2012)CrossRefGoogle Scholar
  18. 18.
    Shi, C., et al.: On selection of objective functions in multi-objective community detection. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management (2011)Google Scholar
  19. 19.
    Shi, C., et al.: Comparison and selection of objective functions in multiobjective community detection. Comput. Intell. 30(3), 562–582 (2014)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Vitali, S., Battiston, S.: The community structure of the global corporate network. PLoS ONE 9(8), e104655 (2014)CrossRefGoogle Scholar
  21. 21.
    Wu, P., Pan, L.: Multi-objective community detection based on memetic algorithm. PLoS ONE 10(5), e0126845 (2015)CrossRefGoogle Scholar
  22. 22.
    Wu, P., Pan, L.: Multi-objective community detection method by integrating users’ behavior attributes. Neurocomputing 210, 13–25 (2016)CrossRefGoogle Scholar
  23. 23.
    Xu, Z., et al.: A model-based approach to attributed graph clustering. In: Proceedings of the ACM SIGMOD International Conference on Management of Data (2012)Google Scholar
  24. 24.
    Yang, J., McAuley, J., Leskovec, J.: Community detection in networks with node attributes. In: IEEE 13th International Conference on Data Mining (ICDM) (2013)Google Scholar
  25. 25.
    Zhang, H., et al.: Semi-supervised distance metric learning based on local linear regression for data clustering. Neurocomputing 93, 100–105 (2012)CrossRefGoogle Scholar
  26. 26.
    Zhang, Q., Li, H.: MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)CrossRefGoogle Scholar
  27. 27.
    Zhou, Y., Cheng, H., Yu, J.X.: Graph clustering based on structural/attribute similarities. Proc. VLDB Endowment 2(1), 718–729 (2009)CrossRefGoogle Scholar
  28. 28.
    Zhou, Y., Cheng, H., Yu, J.X.: Clustering large attributed graphs: an efficient incremental approach. In: 2010 IEEE 10th International Conference on Data Mining (ICDM) (2010)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Alireza Moayedekia
    • 1
  • Kok-Leong Ong
    • 2
  • Yee Ling Boo
    • 3
  • William Yeoh
    • 1
  1. 1.Department of Information Systems and Business AnalyticsDeakin UniversityGeelongAustralia
  2. 2.SAS Analytics Innovation Lab, ASSCLa Trobe UniversityMelbourneAustralia
  3. 3.School of Business IT & LogisticsRMIT UniversityMelbourneAustralia

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