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Community Detection in Complex Networks: Algorithms and Analysis

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Trustworthy Computing and Services (ISCTCS 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 520))

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Abstract

Community structure in networks indicates some meaningful groups or organizations in the real world. Various algorithms to detect community in complex networks were proposed. However, problems about how to judge the goodness and performance of algorithms are still open. This paper reviewed and analyzed the related work and algorithms for community detection, hoping to benefit researchers in related field.

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References

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

    Article  MathSciNet  Google Scholar 

  2. Kernighan, B.W., Lin, S.: An efficient heuristic procedure for partitioning graphs. Bell Syst. Tech. J. 49(2), 291–307 (1970)

    Article  MATH  Google Scholar 

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

    Article  MATH  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  5. Qi, X., Tang, W., Wu, Y., et al.: Optimal local community detection in social networks based on density drop of subgraphs. Pattern Recogn. Lett. 36, 46–53 (2014)

    Article  Google Scholar 

  6. Guimera, R., Amaral, L.A.N.: Functional cartography of complex metabolic networks. Nature 433(7028), 895–900 (2005)

    Article  Google Scholar 

  7. Riedy, J., Bader, D. A., Meyerhenke, H.: Scalable multi-threaded community detection in social networks. In: 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum (IPDPSW), pp. 1619–1628. IEEE (2012)

    Google Scholar 

  8. Ovelgönne, M., Geyer-Schulz, A.: An ensemble learning strategy for graph clustering. Gr. Partitioning Gr. Clustering 588, 187 (2012)

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. Nascimento, M.C.V., Pitsoulis, L.: Community detection by modularity maximization using GRASP with path relinking. Comput. Oper. Res. 40(12), 3121–3131 (2013)

    Article  MathSciNet  Google Scholar 

  11. Palla, G., Derényi, I., Farkas, I., et al.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043), 814–818 (2005)

    Article  Google Scholar 

  12. Xie, J., Kelley, S., Szymanski, B.K.: Overlapping community detection in networks: The state-of-the-art and comparative study. ACM Comput. Surv. (CSUR) 45(4), 43 (2013)

    Article  Google Scholar 

  13. Evans, T.S., Lambiotte, R.: Line graphs, link partitions, and overlapping communities. Phys. Rev. E 80(1), 016105 (2009)

    Article  Google Scholar 

  14. De Meo, P., Ferrara, E., Fiumara, G., et al.: Enhancing community detection using a network weighting strategy. Inf. Sci. 222, 648–668 (2013)

    Article  MATH  Google Scholar 

  15. Lim, S., Ryu, S., Kwon, S., et al.: LinkSCAN*: overlapping community detection using the link-space transformation. In: 2014 IEEE 30th International Conference on Data Engineering (ICDE), pp. 292–303. IEEE (2014)

    Google Scholar 

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

    Article  Google Scholar 

  17. Gregory, S.: Finding overlapping communities in networks by label propagation. New J. Phys. 12(10), 103018 (2010)

    Article  Google Scholar 

  18. Xie, J., Szymanski, B. K., Liu, X.: Slpa: uncovering overlapping communities in social networks via a speaker-listener interaction dynamic process. In: 2011 IEEE 11th International Conference on Data Mining Workshops (ICDMW), pp. 344–349. IEEE, (2011)

    Google Scholar 

  19. Newman, M.E.J.: Community detection and graph partitioning. EPL (Europhys. Lett.) 103(2), 28003 (2013)

    Article  Google Scholar 

  20. Arias-Castro, E., Verzelen, N.: Community detection in dense random networks. Ann. Stat. 42(3), 940–969 (2014)

    Article  MATH  MathSciNet  Google Scholar 

  21. Shi, C., Yan, Z., Shi, Z., et al.: A fast multi-objective evolutionary algorithm based on a tree structure. Appl. Soft Comput. 10(2), 468–480 (2010)

    Article  MathSciNet  Google Scholar 

  22. Shi, C., Kong, X., Fu, D., et al.: Multi-label classification based on multi-objective optimization. ACM Trans. Intell. Syst. Technol. (TIST) 5(2), 35 (2014)

    Google Scholar 

  23. Bassett, D.S., Porter, M.A., Wymbs, N.F., et al.: Robust detection of dynamic community structure in networks. Chaos Interdisc. J. Nonlinear Sci. 23(1), 013142 (2013)

    Article  Google Scholar 

  24. Kim, C.M., Kang, I.S., Han, Y.H., et al.: A community detection scheme in delay-tolerant networks. In: Han, Y.-H., Park, D.-S., Jia, W., Yeo, S.-S. (eds.) Ubiquitous Information Technologies and Applications, pp. 745–751. Springer, Heidelburg (2013)

    Chapter  Google Scholar 

  25. Shi, C., Kong, X., Huang, Y., et al.: HeteSim: a general framework for relevance measure in heterogeneous Networks. IEEE Trans. Knowl. Data Eng. 10, 2479–2492 (2014)

    Article  Google Scholar 

  26. Yang, J., Leskovec, J.: Defining and evaluating network communities based on ground-truth. In: Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics, pp. 3. ACM (2012)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Information Engineering, Minzu University of China, Beijing, China

    Yuan Jie & Liu Zhishuai

  2. Institute of Network and Education Technology, Shandong University of Traditional Chinese Medicine, Jinan, China

    Xiaoyu Qiu

Authors
  1. Yuan Jie
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  2. Liu Zhishuai
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  3. Xiaoyu Qiu
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Corresponding author

Correspondence to Liu Zhishuai .

Editor information

Editors and Affiliations

  1. Beijing University of Posts and Telecommuncations, Beijing, China

    Lu Yueming

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Wu Xu

  3. Beijing University of Posts and Telecommunications, Beijing, China

    Zhang Xi

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Jie, Y., Zhishuai, L., Qiu, X. (2015). Community Detection in Complex Networks: Algorithms and Analysis. In: Yueming, L., Xu, W., Xi, Z. (eds) Trustworthy Computing and Services. ISCTCS 2014. Communications in Computer and Information Science, vol 520. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47401-3_31

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  • DOI: https://doi.org/10.1007/978-3-662-47401-3_31

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-47400-6

  • Online ISBN: 978-3-662-47401-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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