Applied Intelligence

, Volume 48, Issue 5, pp 1314–1326 | Cite as

Overlapping community detection based on discrete biogeography optimization

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Abstract

Community detection can be used to help mine the potential information in social networks, and uncovering community structures in social networks can be regarded as clustering optimization problems. In this paper, an overlapping community detection algorithm based on biogeography optimization is proposed. Firstly, the algorithm takes the method of label propagation based on local max degree and neighborhood overlap for initial network partitioning. The preliminary partition result used to construct initial population by cloning and mutating to accelerate the algorithm’s convergence. Next, to make biogeography optimization algorithm suitable for community detection, we design problem-specific migration rules and mutation operators based on a novel affinity degree to improve the effectiveness of the algorithm. Experiments on benchmark test data, including two synthetic networks and four real-world networks, show that the proposed algorithm can achieve results with better accuracy and stability than the compared evolutionary algorithms.

Keywords

Community detection Local maximum degree node Neighborhood overlap Label propagation Affinity degree 

Notes

Acknowledgments

This work was supported by the Natural Science Foundation of Chongqing Education Commission (No. KJ1601214), and the Research Innovation Platform of Yangtze Normal University (No. 2015XJPT02).

References

  1. 1.
    Cai Q, Gong M, Ma L (2015) Greedy discrete particle swarm optimization for large-scale social network clustering. Inf Sci 316:503–516CrossRefGoogle Scholar
  2. 2.
    Meghanathan N (2016) A greedy algorithm for neighborhood overlap-based community detection. Algorithms 9(1):8–34MathSciNetCrossRefGoogle Scholar
  3. 3.
    Wang Z, Chen Z, Zhao Y, Chen S (2014) A community detection algorithm based on topology potential and spectral clustering. Sci World J 2:329325–329325Google Scholar
  4. 4.
    Ding J, Jiao L, Wu J, Liu F (2016) Prediction of missing links based on community relevance and ruler inference. Knowl-Based Syst 98:200–215CrossRefGoogle Scholar
  5. 5.
    Cheraghchi HS, Zakerolhosseini A (2017) Toward a novel art inspired incremental community mining algorithm in dynamic social network. Appl Intell 46:409–426CrossRefGoogle Scholar
  6. 6.
    Pizzuti C (2012) A multiobjective genetic algorithm to find communities in complex networks. IEEE Trans Evolut Comput 16(3):418–430CrossRefGoogle Scholar
  7. 7.
    Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713CrossRefGoogle Scholar
  8. 8.
    Garg H (2015) An efficient biogeography based optimization algorithm for solving reliability optimization problems. Swarm Evol Comput 24:1–10CrossRefGoogle Scholar
  9. 9.
    Bhattacharya A, Chattopadhyay PK (2010) Solving complex economic load dispatch problems using biogeography-based optimization. Expert Syst Appl 37(5):3605–3615CrossRefGoogle Scholar
  10. 10.
    Hadidi A, Nazari A (2013) Design and economic optimization of shell-and-tube heat exchangers using biogeography-based (BBO) algorithm. Appl Therm Eng 51:1263–1272CrossRefGoogle Scholar
  11. 11.
    Xie J, Kelley S, Szymanski BK (2011) Overlapping community detection in networks: the state of the art and comparative study. ACM Comput Surv 45(4):115–123MATHGoogle Scholar
  12. 12.
    Wang Z-X, Li Z-C, Ding X-f, Tang J-H (2016) Overlapping community detection based on node location analysis. Knowl-Based Syst 150:225–235Google Scholar
  13. 13.
    Wang X, Li J (2013) Detecting communities by the core-vertex and intimate degree in complex networks. Physica A Stat Mech Appl 392:2555–2563CrossRefGoogle Scholar
  14. 14.
    Han Y, Li D, Wang T (2011) Identifying different community members in complex networks based on topology potential. Front Comput Sci China 5(1):87–99MathSciNetCrossRefGoogle Scholar
  15. 15.
    Li J, Wang X, Cui Y (2014) Uncovering the overlapping community structure of complex networks by maximal cliques. Physica A Stat Mech Appl 415:398–406MathSciNetCrossRefGoogle Scholar
  16. 16.
    Cui Y, Wang X, Eustace J (2014) Detecting community structure via the maximal sub-graphs and belonging degrees in complex networks. Physica A Stat Mech Appl 416(C):198–207CrossRefGoogle Scholar
  17. 17.
    Li J, Wang X, Eustace J (2013) Detecting overlapping communities by seed community in weighted complex networks. Physica A Stat Mech Appl 392:6125–6134CrossRefGoogle Scholar
  18. 18.
    Bu Z, Zhang C, Xia Z, Wang J (2013) A fast parallel modularity optimization algorithm (FPMQA) for community detection in online social network. Knowl-Based Syst 50(3):246–259CrossRefGoogle Scholar
  19. 19.
    Hajiabadi M, Zare H, Bobarshad H (2017) IEDC: An integrated approach for overlapping and non-overlapping community detection. Knowl-Based Syst 123(5):188–199CrossRefGoogle Scholar
  20. 20.
    Easley D, Kleinberg J (2010) Networks, Crowds, and Markets:Reasoning about a Highly Connected World, 1st edn. Cambridge University Press, CambridgeCrossRefMATHGoogle Scholar
  21. 21.
    De Meo P, Ferrara E, Fiumara G, Provetti A (2014) On Facebook, Most Ties Are Weak. Commun ACM 57:78–84CrossRefGoogle Scholar
  22. 22.
    Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Natl Acad Sci USA 99(12):7821–7826MathSciNetCrossRefMATHGoogle Scholar
  23. 23.
    Wang X, Duan H (2014) A hybrid biogeography-based optimization algorithm for job shopscheduling problem. Comput Ind Eng 73:96–114CrossRefGoogle Scholar
  24. 24.
    Guo W, Wang L, Wu Q (2016) Numerical comparisons of migration models for Multi-objective Biogeography-Based Optimization. Inf Sci 328:302–320CrossRefGoogle Scholar
  25. 25.
    Hadidi A (2015) A robust approach for optimal design of plate fin heat exchangers using biogeography based optimization (BBO) algorithm. Appl Energy 150:196–210CrossRefGoogle Scholar
  26. 26.
    Shang R, Luo S, Zhang W, Stolkin R, Jiao L (2016) A multiobjective evolutionary algorithm to find community structures based on affinity propagation. Physica A 453:203–227CrossRefGoogle Scholar
  27. 27.
    Zhoua X, Liu Y, Li B, Suna G (2015) Multiobjective biogeography based optimization algorithm with decomposition for community detection in dynamic networks. Physica A 436:430–442CrossRefGoogle Scholar
  28. 28.
    Pizzuti C (2008) GA-Net: a genetic algorithm for community detection in social networks. In: Parallel Problem Solving from Nature (PPSN), vol. 5199, pp 1081–1090Google Scholar
  29. 29.
    Attea BA, Hariz WA, Abdulhalim MF (2016) Improving the performance of evolutionary multi-objective co-clustering models for community detection in complex social networks. Swarm Evol Comput 26:137–156CrossRefGoogle Scholar
  30. 30.
    Xin Y, Xie Z-Q, Yang J (2016) The adaptive dynamic community detection algorithm based on the non-homogeneous random walking. Physica A 450:241–252CrossRefGoogle Scholar
  31. 31.
    Chen J, Wang H, Wang L, Liu W (2016) A dynamic evolutionary clustering perspective: Community detection in signed networks by reconstructing neighbor sets. Physica A 447:482–492MathSciNetCrossRefGoogle Scholar
  32. 32.
    Yang J, Leskovec J (2012) Community-affiliation graph model for overlapping network community detection. In: 2012 IEEE 12th International Conference on Data Mining (ICDM), IEEE, pp 1170–1175Google Scholar
  33. 33.
    Xie J, Szymanski BK (2012) Towards linear time overlapping community detection in social networks. In: Advances in Knowledge Discovery and Data Mining, Springer, pp 25–36Google Scholar
  34. 34.
    Whang J, Gleich D, Dhillon I (2016) Overlapping Community Detection Using Neighborhood-Inflated Seed Expansion. IEEE Trans Knowl Data Eng 28(5):1272–1284CrossRefGoogle Scholar
  35. 35.
    Sobolevsky S, Campari R, Belyi A, Ratti C (2014) General optimization technique for high-quality community detection in complex networks. Phys Rev E 90(1):012811CrossRefGoogle Scholar
  36. 36.
    Fortunato S (2010) Community detection in graphs. Phys Rep 486(3):75–174MathSciNetCrossRefGoogle Scholar
  37. 37.
    Lancichinetti A, Fortunato S, Radicchi F (2008) Benchmark graphs for testing community detection algorithms. Phys Rev E Stat Nonlin Soft Matter Phys 78(2):046110CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.School of Computer EngineeringYangtze Normal UniversityChongqingChina
  2. 2.College of Communication EngineeringChongqing UniversityChongqingChina

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