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Leader-aware community detection in complex networks

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Abstract

Community structures are very common in complex networks. Detecting these communities is important for understanding the hidden features of networks. Besides, each community usually has one leader, which presents its significant influence over the whole community. However, most existing methods just focus on the problem of graph clustering, ignoring the role of community leaders. To solve this problem, in this paper, we propose a novel leader-aware community detection algorithm, which can find community structures as well as leaders of each community. This algorithm measures the leadership of each node and lets each one adhere to its local leader, forming dependence trees. Once all dependence trees are definitely settled, the community structures emerge because one tree actually is a cluster. Additionally, each root node of the tree is exactly the leader of corresponding community. This method can quickly determine the belonging of each node. Experimental results on real-world and benchmark networks demonstrate the effectiveness and the efficiency of our algorithm compared with other state-of-the-art approaches.

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References

  1. Bhatia V, Rani R (2018) Dfuzzy: a deep learning-based fuzzy clustering model for large graphs. Knowl Inf Syst 57:159–181

    Article  Google Scholar 

  2. Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 10:10008

    Article  Google Scholar 

  3. Bu Z, Cao J, Li HJ, Gao G, Tao H (2017) Gleam: a graph clustering framework based on potential game optimization for large-scale social networks. Knowl Inf Syst pp 1–30

  4. Cao S, Lu W, Xu Q (2016) Deep neural networks for learning graph representations. In: AAAI, pp 1145–1152

  5. Capocci A, Servedio VD, Caldarelli G, Colaiori F (2005) Detecting communities in large networks. Phys A Stat Mech Appl 352(2):669–676

    Article  Google Scholar 

  6. Clauset A, Newman ME, Moore C (2004) Finding community structure in very large networks. Phys Rev E 70(6):066111

    Article  Google Scholar 

  7. Costa H, Merschmann LH, Barth F, Benevenuto F (2014) Pollution, bad-mouthing, and local marketing: the underground of location-based social networks. Inf Sci 279:123–137

    Article  Google Scholar 

  8. Ester M, Kriegel HP, Sander J, Xu X, et al (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Kdd, vol 96, pp 226–231

  9. Fortunato S (2010) Community detection in graphs. Phys Rep 486(3):75–174

    Article  MathSciNet  Google Scholar 

  10. Fortunato S, Barthelemy M (2007) Resolution limit in community detection. Proc Natl Acad Sci 104(1):36–41

    Article  Google Scholar 

  11. Froome C, Keys N, Thomsen DC, Smith TF (2010) Opinion leaders and complex sustainability issues. Manag Environ Qual Int J 21(2):187–197

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  13. Gong M, Cai Q, Chen X, Ma L (2014) Complex network clustering by multiobjective discrete particle swarm optimization based on decomposition. IEEE Trans Evolut Comput 18(1):82–97

    Article  Google Scholar 

  14. Goyal A, Bonchi F, Lakshmanan LV (2008) Discovering leaders from community actions. In: Proceedings of the 17th ACM conference on information and knowledge management, ACM, pp 499–508

  15. Guimera R, Amaral LAN (2005) Functional cartography of complex metabolic networks. Nature 433(7028):895–900

    Article  Google Scholar 

  16. Jonnalagadda A, Kuppusamy L (2016) A survey on game theoretic models for community detection in social networks. Soc Netw Anal Min 6(1):83

    Article  Google Scholar 

  17. Khorasgani RR, Chen J, Zaiane OR (2010) Top leaders community detection approach in information networks. In: 4th SNA-KDD workshop on social network mining and analysis, Citeseer

  18. Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907

  19. Lancichinetti A, Fortunato S, Radicchi F (2008) Benchmark graphs for testing community detection algorithms. Phys Rev E 78(4):046110

    Article  Google Scholar 

  20. Li HJ, Bu Z, Li A, Liu Z, Shi Y (2016) Fast and accurate mining the community structure: integrating center locating and membership optimization. IEEE Trans Knowl Data Eng 28(9):2349–2362

    Article  Google Scholar 

  21. Lim S, Kim J, Lee JG (2016) Blackhole: robust community detection inspired by graph drawing. In: 2016 IEEE 32nd international conference on data engineering (ICDE), IEEE, pp 25–36

  22. Liu S, Jiang C, Lin Z, Ding Y, Duan R, Xu Z (2015) Identifying effective influencers based on trust for electronic word-of-mouth marketing: a domain-aware approach. Inf Sci 306:34–52

    Article  Google Scholar 

  23. Liu X, Zhou Y, Hu C, Guan X, Leng J (2014) Detecting community structure for undirected big graphs based on random walks. In: Proceedings of the 23rd international conference on world wide web, ACM, pp 1151–1156

  24. Lu D, Li Q, Liao SS (2012) A graph-based action network framework to identify prestigious members through member’s prestige evolution. Decis Support Syst 53(1):44–54

    Article  Google Scholar 

  25. Lu L, Zhang YC, Yeung CH, Zhou T (2011) Leaders in social networks, the delicious case. PloS ONE 6(6):e21202

    Article  Google Scholar 

  26. Ma T, Xia Z, Yang F (2017) An ant colony random walk algorithm for overlapping community detection. In: International conference on intelligent data engineering and automated learning, Springer, pp 20–26

  27. Ma X, Yu H, Wang Y, Wang Y (2015) Large-scale transportation network congestion evolution prediction using deep learning theory. PloS ONE 10(3):e0119044

    Article  Google Scholar 

  28. Mahmood A, Small M, Al-Maadeed SA, Rajpoot N (2017) Using geodesic space density gradients for network community detection. IEEE Trans Knowl Data Eng 29(4):921–935

    Article  Google Scholar 

  29. Newman ME (2006) Modularity and community structure in networks. Proc Natl Acad Sci 103(23):8577–8582

    Article  Google Scholar 

  30. Newman ME, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69(2):026113

    Article  Google Scholar 

  31. Pal S, Dong Y, Thapa B, Chawla NV, Swami A, Ramanathan R (2016) Deep learning for network analysis: problems, approaches and challenges. In: Military communications conference, MILCOM 2016–2016 IEEE, IEEE, pp 588–593

  32. Pons P, Latapy M (2005) Computing communities in large networks using random walks. In: International symposium on computer and information sciences, Springer, pp 284–293

  33. Raghavan UN, Albert R, Kumara S (2007) Near linear time algorithm to detect community structures in large-scale networks. Phys Rev E 76(3):036106

    Article  Google Scholar 

  34. Rand WM (1971) Objective criteria for the evaluation of clustering methods. J Am Stat Assoc 66(336):846–850

    Article  Google Scholar 

  35. Rosvall M, Bergstrom CT (2008) Maps of random walks on complex networks reveal community structure. Proc Natl Acad Sci 105(4):1118–1123

    Article  Google Scholar 

  36. Schaeffer SE (2007) Graph clustering. Comput Sci Rev 1(1):27–64

    Article  Google Scholar 

  37. Shah D, Zaman T (2010) Community detection in networks: the leader-follower algorithm. arXiv preprint arXiv:10110774

  38. Shao J, Han Z, Yang Q, Zhou T (2015) Community detection based on distance dynamics. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 1075–1084

  39. Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905

    Article  Google Scholar 

  40. Shiokawa H, Fujiwara Y, Onizuka M (2015) Scan++: efficient algorithm for finding clusters, hubs and outliers on large-scale graphs. Proc VLDB Endow 8(11):1178–1189

    Article  Google Scholar 

  41. Simonsen I (2005) Diffusion and networks: a powerful combination!. Phys A Stat Mech Appl 357(2):317–330

    Article  Google Scholar 

  42. Strehl A, Ghosh J (2002) Cluster ensembles–a knowledge reuse framework for combining multiple partitions. J Mach Learn Res 3(Dec):583–617

    MathSciNet  MATH  Google Scholar 

  43. Sun H, Huang J, Han J, Deng H, Zhao P, Feng B (2010) gskeletonclu: density-based network clustering via structure-connected tree division or agglomeration. In: 2010 IEEE 10th international conference on data mining (ICDM), IEEE, pp 481–490

  44. Tian F, Gao B, Cui Q, Chen E, Liu TY (2014) Learning deep representations for graph clustering. In: AAAI, pp 1293–1299

  45. Von Luxburg U (2007) A tutorial on spectral clustering. Stat Comput 17(4):395–416

    Article  MathSciNet  Google Scholar 

  46. White S, Smyth P (2005) A spectral clustering approach to finding communities in graphs. In: Proceedings of the 2005 SIAM international conference on data mining, SIAM, pp 274–285

  47. Xu X, Yuruk N, Feng Z, Schweiger TA (2007) Scan: a structural clustering algorithm for networks. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 824–833

  48. Yakoubi Z, Kanawati R (2014) Licod: a leader-driven algorithm for community detection in complex networks. Vietnam J Comput Sci 1(4):241–256

    Article  Google Scholar 

  49. Yang L, Cao X, He D, Wang C, Wang X, Zhang W (2016) Modularity based community detection with deep learning. In: Proceedings of the 25th international joint conference on artificial intelligence, pp 2252–2258

  50. Zachary WW (1977) An information flow model for conflict and fission in small groups. J Anthropol Res 33(4):452–473

    Article  Google Scholar 

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Acknowledgements

The work was supported in part by the National Science Foundation of China Grants 61672417, 61876138, 61602354 and 61472299, Natural Science Basic Research Plan in Shaanxi Province of China Grants 2017JM6045 and the Fundamental Research Funds for the Central Universities of China, Science and Technology Foundation of Shenzhen City Grants JCYJ20170816100845994. Any opinions, findings and conclusions expressed here are those of the authors and do not necessarily reflect the views of the funding agencies.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, China

    Heli Sun, Hongxia Du, Yang Li, Zhongbin Sun, Liang He, Xiaolin Jia & Zhongmeng Zhao

  2. Xi’an Jiaotong University Shenzhen Research School, Shenzhen, 518057, Guangdong, China

    Heli Sun & Zhongbin Sun

  3. Shaanxi Province Key Laboratory of Computer Networks, Xi’an Jiaotong University, Xi’an, China

    Heli Sun

  4. School of Computer Science and Technology, Xidian University, Xi’an, Shaanxi, China

    Jianbin Huang

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  1. Heli Sun
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  2. Hongxia Du
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Correspondence to Jianbin Huang.

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Sun, H., Du, H., Huang, J. et al. Leader-aware community detection in complex networks. Knowl Inf Syst 62, 639–668 (2020). https://doi.org/10.1007/s10115-019-01362-1

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