Community detection in large-scale social networks: state-of-the-art and future directions

  • Mehdi AzaouziEmail author
  • Delel Rhouma
  • Lotfi Ben Romdhane
Original Article


Community detection is an important research area in social networks analysis where we are concerned with discovering the structure of the social network. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is an NP-hard problem and not yet solved to a satisfactory level. This computational complexity is hampered by two major factors. The first factor is related to the huge size of nowadays social networks like Facebook and Twitter reaching billions of nodes. The second factor is related to the dynamic nature of social networks whose structure evolves over time. For this, community detection in social networks analysis is gaining increasing attention in the scientific community and a lot of research was done in this area. The main goal of this paper is to give a comprehensive survey of community detection algorithms in social graphs. For this, we provide a taxonomy of existing models based on the computational nature (either centralized or distributed) and thus in static and dynamic social networks. In addition, we provide a comprehensive overview of existing applications of community detection in social networks. Finally, we provide further research directions as well as some open challenges.


Social networks Dynamic social network Centralized community detection Distributed community detection Semantic 



  1. Abrouk L, Gross-Amblard D, Leprovost D (2010) Decouverte de communautes par analyse des usages. extraction et gestion des connaissances-Atelier Web Social A5–5Google Scholar
  2. Aktunc R, Toroslu IH, Ozer M, Davulcu H (2015) A dynamic modularity based community detection algorithm for large-scale networks: Dslm. In: Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining, ASONAM’15, pp 1177–1183Google Scholar
  3. Albert R, Barabási A-L (2002) Statistical mechanics of complex networks. Rev Mod Phys 74(1):47–97MathSciNetzbMATHGoogle Scholar
  4. Asadi M, Ghaderi F (2018) Incremental community detection in social networks using label propagation method. In: Proceedings of the 2018 23rd conference of open innovations association, FRUCT’18, pp 39–47Google Scholar
  5. Azaouzi M, Romdhane LB (2017) An evidential influence-based label propagation algorithm for distributed community detection in social networks. Procedia Computer Science, 112:407 – 416. In: Proceedings of the 21st international conference on knowledge-based and intelligent information and engineering systems, KES2017, 6–8 Sep 2017, Marseille, FranceGoogle Scholar
  6. Azaouzi M, Romdhane LB (2018) An efficient two-phase model for computing influential nodes in social networks using social actions. J Comput Sci Technol 33(2):286–304MathSciNetGoogle Scholar
  7. Barnard ST, Simon HD (1994) Fast multilevel implementation of recursive spectral bisection for partitioning unstructured problems. Concurr Comput Pract Exp 6(2):101–117Google Scholar
  8. Ben Romdhane L, Chaabani Y, Zardi H (2013) A robust ant colony optimization-based algorithm for community mining in large scale oriented social graphs. Expert Syst Appl 40(14):5709–5718Google Scholar
  9. Bezdek JC, Ehrlich R, Full W (1984) Fcm: the fuzzy c-means clustering algorithm. Comput Geosci 10(2–3):191–203Google Scholar
  10. Bhat SY, Abulaish M (2015) Hoctracker: tracking the evolution of hierarchical and overlapping communities in dynamic social networks. IEEE Trans Knowl Data Eng 27(4):1013–1019Google Scholar
  11. Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E (2008a) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 2008(10):P10008Google Scholar
  12. Boppana RB (1987) Eigenvalues and graph bisection: an average-case analysis. In: 28th annual symposium on foundations of computer science, pp 280–285Google Scholar
  13. Bu Z, Wu Z, Cao J, Jiang Y (2016) Local community mining on distributed and dynamic networks from a multiagent perspective. IEEE Trans Cybern 46(4):986–999Google Scholar
  14. Buzun N, Korshunov A, Avanesov V, Filonenko I, Kozlov I, Turdakov D, Kim H (2014) Egolp: fast and distributed community detection in billion-node social networks. In: Proceedings of the 2014 IEEE international conference on data mining workshop, ICDMW’14, pp 533–540Google Scholar
  15. Calderone A, Formenti M, Aprea F, Papa M, Alberghina L, Colangelo AM, Bertolazzi P (2016) Comparing alzheimer’s and parkinson’s diseases networks using graph communities structure. BMC Syst Biol 10(1):25Google Scholar
  16. Cattuto C, Baldassarri A, Servedio VD, Loreto V (2008) Emergent community structure in social tagging systems. Adv Complex Syst 11(04):597–608zbMATHGoogle Scholar
  17. Chaabani Y, Akaichi J (2017) Meaningful communities detection in medias network. Soc Netw Anal Min 7(1):1–11Google Scholar
  18. Chakraborty T, Dalmia A, Mukherjee A, Ganguly N (2017) Metrics for community analysis: a survey. ACM Comput Surv 50(4):54:1–54:37Google Scholar
  19. Charikar MS (2002) Similarity estimation techniques from rounding algorithms. In: Proceedings of the thiry-fourth annual ACM symposium on theory of computing, STOC’02, pp 380–388Google Scholar
  20. Chevalier C, Safro I (2009) Comparison of coarsening schemes for multilevel graph partitioning. In: Learning and intelligent optimization, pp 191–205Google Scholar
  21. Clementi A, Di Ianni M, Gambosi G, Natale E, Silvestri R (2015) Distributed community detection in dynamic graphs. Theor Comput Sci 584:19–41MathSciNetzbMATHGoogle Scholar
  22. Collingsworth B, Menezes R (2014) A self-organized approach for detecting communities in networks. Soc Netw Anal Min 4(1):169Google Scholar
  23. Cordeiro M, Sarmento RP, Gama J (2016) Dynamic community detection in evolving networks using locality modularity optimization. Soc Netw Anal Min 6(1):15Google Scholar
  24. Costa LF, Oliveira ON Jr, Travieso G, Rodrigues FA, Villas Boas PR, Antiqueira L, Viana MP, CorreaRocha LE (2011) Analyzing and modeling real-world phenomena with complex networks: a survey of applications. Adv Phys 60(3):329–412Google Scholar
  25. Cruz JD, Bothorel C, Poulet F (2011) Semantic clustering of social networks using points of view. CORIA: conférence en recherche d’information et applications. Avignon, France, pp 175–182Google Scholar
  26. Dang T, Viennet E (2012) Community detection based on structural and attribute similarities. In: International conference on digital society, ICDS’12, pp 7–14Google Scholar
  27. Dhillon IS, Guan Y, Kulis B (2007) Weighted graph cuts without eigenvectors: a multilevel approach. IEEE Transa Pattern Anal Mach Intell 29(11):1944–1957Google Scholar
  28. Faloutsos M, Faloutsos P, Faloutsos C (1999) On power–law relationships of the internet topology. SIGCOMM Comput Commun Rev 29(4):251–262zbMATHGoogle Scholar
  29. Fan W, Yeung K (2014) Incorporating profile information in community detection for online social networks. Phys A Stat Mech Appl 405:226–234Google Scholar
  30. Feng H, Tian J, Wang HJ, Li M (2015) Personalized recommendations based on time-weighted overlapping community detection. Inf Manag 52(7):789–800Google Scholar
  31. Fortunato S (2010) Community detection in graphs. Phys Rep 486(3):75–174MathSciNetGoogle Scholar
  32. Galluzzi V (2012) Real time distributed community structure detection in dynamic networks. In: 2012 IEEE/ACM international conference on advances in social networks analysis and mining, ASONAM’12, pp 1236–1241Google Scholar
  33. Gargi U, Lu W, Mirrokni VS, Yoon S (2011) Large-scale community detection on youtube for topic discovery and exploration. In: Proceedings of the fifth international conference on weblogs and social media, ICWSM’11, pp 486–489Google Scholar
  34. Gasparetti F, Micarelli A, Sansonetti G (2017) Community detection and recommender systems. Springer, New York, pp 1–14Google Scholar
  35. Ge R, Ester M, Gao BJ, Hu Z, Bhattacharya B, Ben-Moshe B (2008) Joint cluster analysis of attribute data and relationship data: the connected k-center problem, algorithms and applications. ACM Trans Knowl Discov Data 2(2):7:1–7:35Google Scholar
  36. Ghaemmaghami F, Sarhadi RM (2013) Somsn: an effective self organizing map for clustering of social networks. Int J Comput Appl 84(5):7–12Google Scholar
  37. Girvan M, Newman ME (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99(12):7821–7826MathSciNetzbMATHGoogle Scholar
  38. Gkini C, Brailas A (2015) Visualizations of personal social networks on facebook and community structure: an exploratory study. Eur J Soc Behav 2(1):21–30Google Scholar
  39. Greene D, Doyle D, Cunningham P (2010) Tracking the evolution of communities in dynamic social networks. In: Proceedings of the 2010 international conference on advances in social networks analysis and mining, ASONAM’10, pp 176–183Google Scholar
  40. Gregori E, Lenzini L, Mainardi S (2013) Parallel k-clique community detection on large-scale networks. IEEE Trans Parallel Distrib Syst 24(8):1651–1660Google Scholar
  41. Gu Y, Qian X, Li Q, Wang M, Hong R, Tian Q (2015) Image annotation by latent community detection and multikernel learning. IEEE Trans Image Process 24(11):3450–3463MathSciNetzbMATHGoogle Scholar
  42. Halalai R, Lemnaru C, Potolea R (2010) Distributed community detection in social networks with genetic algorithms. In: Proceedings of the 2010 IEEE international conference on intelligent computer communication and processing, ICCP’10, pp 35–41Google Scholar
  43. He J, Chen D (2015) A fast algorithm for community detection in temporal network. Phys A Stat Mech Appl 429:87–94Google Scholar
  44. Hendrickson B, Leland R (1995) A multilevel algorithm for partitioning graphs. In: Proceedings of the 1995 ACM/IEEE conference on supercomputing, supercomputing’95, p 28Google Scholar
  45. Hopcroft J, Khan O, Kulis B, Selman B (2004) Tracking evolving communities in large linked networks. Proc Natl Acad Sci 101(suppl 1):5249–5253Google Scholar
  46. Hu P, Lau WC (2013) A survey and taxonomy of graph sampling. arXiv:1308.5865
  47. Huang HH, Yang HC (2012) Semantic clustering-based community detection in an evolving social network. In: 2012 sixth international conference on genetic and evolutionary computing, ICGEC’12, pp 91–94Google Scholar
  48. Huang J, Yang B, Jin D, Yang Y (2013) Decentralized mining social network communities with agents. Math Comput Model 57(11):2998–3008MathSciNetzbMATHGoogle Scholar
  49. Hübler C, Kriegel HP, Borgwardt K, Ghahramani Z (2008) Metropolis algorithms for representative subgraph sampling. In: Proceedings of the 2008 eighth IEEE international conference on data mining, ICDM ’08, pp 283–292Google Scholar
  50. Hui P, Yoneki E, Chan SY, Crowcroft J (2007) Distributed community detection in delay tolerant networks. In: Proceedings of 2nd ACM/IEEE international workshop on Mobility in the evolving internet architecture, MobiArch’07, pp 7Google Scholar
  51. Java A, Song X, Finin T, Tseng B (2007) Why we twitter: understanding microblogging usage and communities. In: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis. ACM, pp 56–65Google Scholar
  52. Ji J, Jiao L, Yang C, Liu J (2016) A multiagent evolutionary method for detecting communities in complex networks. Comput Intell 32(4):587–614MathSciNetGoogle Scholar
  53. Kang U, Faloutsos C (2011) Beyond’caveman communities’: hubs and spokes for graph compression and mining. In: Proceedings of the 2011 IEEE 11th international conference on data mining, ICDM’11, pp 300–309Google Scholar
  54. Karypis G, Kumar V (1998a) A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J Sci Comput 20(1):359–392MathSciNetzbMATHGoogle Scholar
  55. Karypis G, Kumar V (1998b) Multilevel k-way partitioning scheme for irregular graphs. J Parallel Distrib Comput 48(1):96–129zbMATHGoogle Scholar
  56. Kernighan BW, Lin S (1970) An efficient heuristic procedure for partitioning graphs. Bell Syst Tech J 49(2):291–307zbMATHGoogle Scholar
  57. Kohonen T, Kaski S, Lappalainen H (1997) Self-organized formation of various invariant-feature filters in the adaptive-subspace som. Neural Comput 9(6):1321–1344Google Scholar
  58. Kosmides P, Adamopoulou E, Demestichas K, Remoundou C, Loumiotis I, Theologou M (2014) Community awareness in academic social networks. In: 2014 IEEE/ACM 7th international conference on utility and cloud computing, pp 647–651Google Scholar
  59. Kothapalli K, Pemmaraju SV, Sardeshmukh V (2013) On the analysis of a label propagation algorithm for community detection. In: Proceedings of the 14th international conference on distributed computing and networking, ICDCN’13, pp 255–269Google Scholar
  60. Krishnamurthy V, Faloutsos M, Chrobak M, Lao L, Cui J-H, Percus AG (2005) Reducing large internet topologies for faster simulations. Networking 5:328–341Google Scholar
  61. Kuzmin K, Shah SY, Szymanski BK (2013) Parallel overlapping community detection with slpa. In: Proceedings of the 2013 international conference on social computing, SocialCom’13, pp 204–212Google Scholar
  62. Lancichinetti A, Fortunato S, Kertész J (2009) Detecting the overlapping and hierarchical community structure in complex networks. New J Phys 11(3):033015Google Scholar
  63. LaSalle D, Karypis G (2015) Multi-threaded modularity based graph clustering using the multilevel paradigm. J Parallel Distrib Comput 76:66–80Google Scholar
  64. Leskovec J, Faloutsos C (2006) Sampling from large graphs. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining, KDD’06, pp 631–636Google Scholar
  65. Leung IX, Hui P, Lio P, Crowcroft J (2009) Towards real-time community detection in large networks. Phys Rev E 79(6):066107Google Scholar
  66. Li S, Lou H, Jiang W, Tang J (2015) Detecting community structure via synchronous label propagation. Neurocomputing 151:1063–1075Google Scholar
  67. Li Y, Liu G, Lao S-Y (2013) A genetic algorithm for community detection in complex networks. J Central South Univ 20(5):1269–1276Google Scholar
  68. Li Z, Liu J, Wu K (2018) A multiobjective evolutionary algorithm based on structural and attribute similarities for community detection in attributed networks. IEEE Trans Cybern 48(7):1963–1976Google Scholar
  69. Li Z, Wang R, Zhang X, Chen L (2010) Self-organizing map of complex networks for community detection. J Syst Sci Complex 23(5):931–941MathSciNetzbMATHGoogle Scholar
  70. Lin Y-R, Chi Y, Zhu S, Sundaram H, Tseng BL (2009) Analyzing communities and their evolutions in dynamic social networks. ACM Trans Knowl Discov Data 3(2):8Google Scholar
  71. Lin Z, Zheng X, Xin N, Chen D (2014) Ck-lpa: Efficient community detection algorithm based on label propagation with community kernel. Phys A Stat Mech Appl 416:386–399Google Scholar
  72. Liu J, Zeng J (2010) Community detection based on modularity density and genetic algorithm. In: Proceedings of the 2010 international conference on computational aspects of social networks, CASoN’10, pp 29–32Google Scholar
  73. Lou H, Li S, Zhao Y (2013) Detecting community structure using label propagation with weighted coherent neighborhood propinquity. Phys A Stat Mech Appl 392(14):3095–3105Google Scholar
  74. Lumsdaine A, Gregor D, Hendrickson B, Berry JW (2007) Challenges in parallel graph processing. Parallel Process Lett 17(1):5–20MathSciNetGoogle Scholar
  75. Maiya AS, Berger-Wolf TY (2010) Sampling community structure. In: Proceedings of the 19th international conference on World wide web, WWW’10, pp 701–710Google Scholar
  76. Mansour N, Ponnusamy R, Choudhary A, Fox GC (1993) Graph contraction for physical optimization methods: a quality-cost tradeoff for mapping data on parallel computers. In: Proceedings of the 7th international conference on supercomputing, ICS’93, pp 1–10Google Scholar
  77. McDaid A, Hurley N (2010) Detecting highly overlapping communities with model-based overlapping seed expansion. In: Proceedings of the 2010 international conference on advances in social networks analysis and mining, ASONAM’10, pp 112–119Google Scholar
  78. Milgram S (1967) The small world problem. Psychol Today 67(1):61–67Google Scholar
  79. Nath K, Roy S (2018) A parallel approach to detect communities in evolving networks. In: Proceedings of the international conference on big data analytics, BDA’18, pp 188–203Google Scholar
  80. Nayak V, Biswas B (2014) Finding prominent features in communities in social networks using ontology, pp 31–36Google Scholar
  81. Neville J, Adler M, Jensen D (2003) Clustering relational data using attribute and link information. In: Proceedings of the text mining and link analysis workshop, 18th international joint conference on artificial intelligence, pp 9–15Google Scholar
  82. Newman ME, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69(2):026113Google Scholar
  83. Nguyen NP, Dinh TN, Shen Y, Thai MT (2014) Dynamic social community detection and its applications. PLoS ONE 9(4):e91431Google Scholar
  84. Noack A, Rotta R (2009) Multi-level algorithms for modularity clustering. SEA 9:257–268Google Scholar
  85. Osborne F, Scavo G, Motta E (2014) A hybrid semantic approach to building dynamic maps of research communities. In: International conference on knowledge engineering and knowledge management, EKAW’15, pp 356–372Google Scholar
  86. Raghavan UN, Albert R, Kumara S (2007) Near linear time algorithm to detect community structures in large-scale networks. Phys Rev E 76(3):036106Google Scholar
  87. Ren Y, Chuah MC, Yang J, Chen Y (2011) Distributed spatio-temporal social community detection leveraging template matching. In: Proceedings of the 2011 IEEE global telecommunications conference, GLOBECOM’11, pp 1–6Google Scholar
  88. Rhouma D, Romdhane LB (2014) An efficient algorithm for community mining with overlap in social networks. Expert Syst Appl 41(9):4309–4321Google Scholar
  89. Rhouma D, Romdhane LB (2018) An efficient multilevel scheme for coarsening large scale social networks. Appl Intell 48(10):3557–3576Google Scholar
  90. Riedy EJ, Meyerhenke H, Ediger D, Bader DA (2011) Parallel community detection for massive graphs. In: Proceedings of the 9th international conference on parallel processing and applied mathematics, pp 286–296zbMATHGoogle Scholar
  91. Ruan Y, Fuhry D, Liang J, Wang Y, Parthasarathy S (2015) Community discovery: simple and scalable approaches. In: User community discovery. Springer, pp 23–54Google Scholar
  92. Sadi S, Ögüdücü Ş, Uyar A. Ş (2010) An efficient community detection method using parallel clique-finding ants. In: Proceedings of the 2010 IEEE congress on evolutionary computation, CGC’10, pp 1–7Google Scholar
  93. Safro I, Ron D, Brandt A (2009) Multilevel algorithms for linear ordering problems. JEA 13:4:1.4–4:1.20MathSciNetzbMATHGoogle Scholar
  94. Said A, Abbasi RA, Maqbool O, Daud A, Aljohani NR (2018) Cc-ga: a clustering coefficient based genetic algorithm for detecting communities in social networks. Appl Soft Comput 63:59–70Google Scholar
  95. Salton G, McGill MJ (1986) Introduction to modern information retrieval. McGraw-Hill Inc, New YorkzbMATHGoogle Scholar
  96. Saltz M, Prat-Pérez A, Dominguez-Sal D (2015) Distributed community detection with the wcc metric. In: Proceedings of the 24th international conference on world wide web, WWW’15, pp 1095–1100Google Scholar
  97. Samie ME, Hamzeh A (2018) Change-aware community detection approach for dynamic social networks. Appl Intell 48(1):78–96Google Scholar
  98. Satuluri V, Parthasarathy S (2009) Scalable graph clustering using stochastic flows: applications to community discovery. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, KDD’09, pp 737–746Google Scholar
  99. Satuluri V, Parthasarathy S, Ruan Y (2011) Local graph sparsification for scalable clustering. In: Proceedings of the 2011 ACM SIGMOD international conference on management of data, SIGMOD’11, pp 721–732Google Scholar
  100. Shang J, Liu L, Li X, Xie F, Wu C (2016) Targeted revision: a learning-based approach for incremental community detection in dynamic networks. Phys A Stat Mech Appl 443:70–85Google Scholar
  101. Shannon CE (2001) A mathematical theory of communication. ACM SIGMOBILE Mob Comput Commun Rev 5(1):3–55MathSciNetGoogle Scholar
  102. Staudt CL, Meyerhenke H (2013) Engineering high-performance community detection heuristics for massive graphs. In: Proceedings of the 2013 42nd international conference on parallel processing, ICPP’13, pp 180–189Google Scholar
  103. Staudt CL, Meyerhenke H (2016) Engineering parallel algorithms for community detection in massive networks. IEEE Trans Parallel Distrib Syst 27(1):171–184Google Scholar
  104. Steinhaeuser K, Chawla NV (2008) Community detection in a large real-world social network. In: Liu H, Salerno JJ, Young MJ (eds) Social computing, behavioral modeling, and prediction. Springer, Boston, MA, pp 168–175Google Scholar
  105. Šubelj L, Bajec M (2011) Robust network community detection using balanced propagation. Eur Phys J B Condens Matter Complex Syst 81(3):353–362Google Scholar
  106. Waltman L, van Eck NJ (2013) A smart local moving algorithm for large-scale modularity-based community detection. Eur Phys J B 86(11):471Google Scholar
  107. Wang T, Chen Y, Zhang Z, Xu T, Jin L, Hui P, Deng B, Li X (2011) Understanding graph sampling algorithms for social network analysis. In: 2011 31st international conference on distributed computing systems workshops, ICDCSW’11, pp 123–128Google Scholar
  108. Wang C-D, Lai J-H, Philip SY (2014a) Neiwalk: community discovery in dynamic content-based networks. IEEE Trans Knowl Data Eng 26(7):1734–1748Google Scholar
  109. Wang Z, Zhang D, Zhou X, Yang D, Yu Z, Yu Z (2014b) Discovering and profiling overlapping communities in location-based social networks. IEEE Trans Syst Man Cybern Syst 44(4):499–509Google Scholar
  110. Wang W, Jiao P, He D, Jin D, Pan L, Gabrys B (2016a) Autonomous overlapping community detection in temporal networks. Knowl Based Syst 110(C):121–134Google Scholar
  111. Wang X, Jin D, Cao X, Yang L, Zhang W (2016b) Semantic community identification in large attribute networks. In: Proceedings of the thirtieth conference on artificial intelligence, AAAI’16, pp 265–271Google Scholar
  112. Wasserman S, Faust K (1994) Social network analysis: methods and applications, volume 8 of structural analysis in the social sciences. Cambridge University Press, CambridgeGoogle Scholar
  113. Whang JJ, Sui X, Dhillon IS (2012) Scalable and memory-efficient clustering of large-scale social networks. In: 2012 IEEE 12th international conference on data mining, ICDM’12, pp 705–714Google Scholar
  114. Whitbeck J, Conan V, Dias de Amorim M (2011) Performance of opportunistic epidemic routing on edge-markovian dynamic graphs. IEEE Trans Commun 59(5):1259–1263Google Scholar
  115. Wu Z, Zou M (2014) An incremental community detection method for social tagging systems using locality-sensitive hashing. Neural Netw 58:14–28Google Scholar
  116. Xie J, Szymanski BK (2013) Labelrank: a stabilized label propagation algorithm for community detection in networks. In: Proceedings of 2013 IEEE 2nd network science workshop, NSW’13, pp 138–143Google Scholar
  117. Xie J, Chen M, Szymanski BK (2013) Labelrankt: incremental community detection in dynamic networks via label propagation. In: Proceedings of the ACM SIGMOD workshop on dynamic networks management and mining, DyNetMM’13, pp 25–32Google Scholar
  118. Yang B, Liu J (2007) An autonomy oriented computing (aoc) approach to distributed network community mining. In: First international conference on self-adaptive and self-organizing systems, SASO’07, pp 151–160Google Scholar
  119. Yang B, Huang J, Liu D, Liu J (2009) A multi-agent based decentralized algorithm for social network community mining. In: 2009 international conference on advances in social network analysis and mining, ASONAM’09, pp 78–82Google Scholar
  120. Yang B, Liu D, Liu J (2010) Discovering communities from social networks: methodologies and applications. In: Furht B (ed) Handbook of social network technologies and applications. Springer, Boston, MA, pp 331–346Google Scholar
  121. Zhang Y, Wang J, Wang Y, Zhou L (2009) Parallel community detection on large networks with propinquity dynamics. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, KDD’15, pp 997–1006Google Scholar
  122. Zhao Z, Feng S, Wang Q, Huang JZ, Williams GJ, Fan J (2012) Topic oriented community detection through social objects and link analysis in social networks. Knowl Based Syst 26:164–173Google Scholar
  123. Zhou Y, Cheng H, Yu JX (2009) Graph clustering based on structural/attribute similarities. Proc VLDB Endow 2(1):718–729Google Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Modeling of Automated Reasoning Systems Research Laboratory LR17ES05, Higher Institute of Computer Science and TelecomUniversity of SousseSousseTunisia

Personalised recommendations