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Node attraction-facilitated evolution algorithm for community detection in networks

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Abstract

Network model recently has become a popular tool for studying complex systems. Detecting meaningful natural groups of nodes called communities in complex networks is an important task in network modeling and analysis. In this paper, the automatic network community detection is formulated as an optimization problem facilitated by node attraction. The basic idea is envision a network as a system of nodes where each node is attracted by its local neighbors. An evolution community detection algorithm is introduced, which employs a metric, named modularity Q as the fitness function and applies node attraction and modularity-based grouping crossover operator. The proposed algorithm faithfully captures the natural communities with high quality. Node attraction is easy to use for the speed up of the convergence of evolution algorithm to better partitions and for making the algorithm more stable. Node attraction does not require any threshold value. Experiments on synthetic and real-world networks further demonstrate the effectiveness of the proposed approach.

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References

  • Brandes U, Delling D, Gaertler M, Gorke R, Hoefer M, Nikoloski Z, Wagner D (2008) On modularity clustering. IEEE Trans Knowl Data Eng 20(2):172–188

    Article  MATH  Google Scholar 

  • Danon L, Diaz-Guilera A, Duch J, Arenas A (2005) Comparing community structure identification. J Stat Mech: Theory Exp 2005(09):P0900

    Article  Google Scholar 

  • Deb K (2001) Multi-Objective Optimization using Evolutionary Algorithms. Wiley, Chichester

    MATH  Google Scholar 

  • Deb K, Pratap A, Agarwal SA, Meyarivan T (2002) A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  • Dorogovtsev SN, Mendes JFF (2003) Evolution of Networks: From Biological Nets to the Internet and WWW. Oxford University Press, Oxford

    Book  MATH  Google Scholar 

  • Fortunato S (2010) Community detection in graphs. Phys Rep 486(3):75–114

    Article  MathSciNet  Google Scholar 

  • Fortunato S, Barthélemy M (2007) Resolution limit in community detection. Proc Natl Acad Sci U S A 104(1):36–41

    Article  Google Scholar 

  • Garey MR, Johnson DS (1979) Computers and intractability: a guide to the theory of NP completeness. W. H. Freeman, New York

    MATH  Google Scholar 

  • Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Natl Acad Sci USA 99:7821–7826

    Article  MathSciNet  MATH  Google Scholar 

  • Girvan M, Newman ME (2002) Community structure in social and biological networks. Proc Natl Acad Sci USA 99(12):7821–7826

    Article  MathSciNet  MATH  Google Scholar 

  • Gong M, Fu B, Jiao L, Du H (2011) A memetic algorithm for community detection in networks. Phys Rev E 84(5):05610

    Article  Google Scholar 

  • Gong M, Chen X, Ma L, Zhang Q, Jiao L (2013) Identification of multi-resolution network structures with multi-objective immune algorithm. Appl Soft Comput 13:1705–1717

    Article  Google Scholar 

  • Hu Y, Yang Bo, Wong H-S (2016) A weighted local view method based on observation over ground truth for community detection. Inf Sci 355–356:37–57

    Article  Google Scholar 

  • Li Y, Liu J, Liu C (2014) A comparative analysis of evolutionary and memetic algorithms for community detection from signed social networks. Soft Comput 18(2):329–348

    Article  Google Scholar 

  • Lu Z, Sun X, Wen Y, Cao G, La Porta T (2015) Algorithms and applications for community detection in weighted networks. IEEE Trans Parallel Distrib Syst 26(11):2916–2926

    Article  Google Scholar 

  • Lusseau D, Schneider K, Boisseau OJ, Haase P, Slooten E, Dawson SM (2003) The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations. Behav Ecol Sociobiol 54:396–405

    Article  Google Scholar 

  • Ma L, Gong M, Liu J, Cai Q, Jiao L (2014) Multi-level learning based memetic algorithm for community detection. Appl Soft Comput 19:121–133

    Article  Google Scholar 

  • Newman MEJ (2001) The structure and function of complex networks. SIAM Rev 2(45):167–256

    MathSciNet  MATH  Google Scholar 

  • Park Y, Song M (1998) A genetic algorithm for clustering problems. In: Proceedings of the third annual conference on genetic programming. pp 568–557

  • Pizzuti C (2008) Ga-net: a genetic algorithm for community detection in social networks. In: PPSN. pp 1081–1090

  • Pizzuti C (2012) A multiobjective genetic algorithm to find communities in complex networks. IEEE Trans Evol Comput 16:418–430

    Article  Google Scholar 

  • Pizzuti C (2017) Evolutionary computation for community detection in networks: a review. Trans Evolut Comput. https://doi.org/10.1109/TEVC.2017.2737600

    Google Scholar 

  • Pothen A, Sinmon H, Liou K-P (1990) Partitioning sparse matrices with eigenvectors of graphs. SIAM J Matrix Anal Appl 11:430–452

    Article  MathSciNet  MATH  Google Scholar 

  • Radicchi F, Castellano C, Cecconi F, Loreto V, Parisi D (2004) Defining and identifying clusters in networks. Proc Natl Acad Sci USA 9(101):2658–2663

    Article  Google Scholar 

  • Rizman Žalik K, Žalik B (2017) Multi-objective evolutionary algorithm using problem-specific genetic operators for community detection in networks. Neural Comput Appl. https://doi.org/10.1007/s00521-017-2884-0

    MATH  Google Scholar 

  • RizmanŽalik K (2015) Maximal neighbor similarity reveals real communities in networks. Sci Rep 5:1837

    Google Scholar 

  • Schuetz P, Caflish A (2008) Efficient modularity optimization by multistep greedy algorithm and node refinement. Phys Rev E 77(4):046112

    Article  Google Scholar 

  • Shi J, Malik J (1997) Normalized cuts and image segmentation. IEEE Trans PAMI 22:888–905

    Google Scholar 

  • Shi C, Yan Z, Cai Y, Wu B (2012) Multi-objective community detection in complex networks. Appl Soft Comput 12:850–85

    Article  Google Scholar 

  • Srinivas N, Deb Kalyanmoy (1994) Multiobjective optimization using nondominated sorting in genetic algorithms. Evolitionary Computation 2(3):221–248

    Article  Google Scholar 

  • Strogatz SH (2001) Exploring complex networks. Nature 410:268–276

    Article  MATH  Google Scholar 

  • Tasgin M, Herdagdelen A, Bingol H (2007) Community detection in complex networks using genetic algorithms. arXiv: 0711.0491

  • Wu P, Pan L (2015) Multi-objective community detection based on memetic algorithm. PloS ONE 10:e0126845

    Article  Google Scholar 

  • Zachary WW (1977) An information flow model for conflict and fission in small groups. J Anthropol Res 33:452–473

    Article  Google Scholar 

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Acknowledgements

This work was supported by the Slovenian Research Agency (grant numbers: P2-0041, J2-8176).

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Authors and Affiliations

  1. Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia

    Krista Rizman Žalik & Borut Žalik

  2. Faculty of Natural Science and Mathematics, University of Maribor, Maribor, Slovenia

    Krista Rizman Žalik

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  1. Krista Rizman Žalik
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  2. Borut Žalik
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Correspondence to Krista Rizman Žalik.

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Rizman Žalik, K., Žalik, B. Node attraction-facilitated evolution algorithm for community detection in networks. Soft Comput 23, 6135–6143 (2019). https://doi.org/10.1007/s00500-018-3267-x

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  • DOI: https://doi.org/10.1007/s00500-018-3267-x

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