Abstract
Community mining is one of the most popular issues in social network analysis. Although various changes may occur in a dynamic social network, they can be classified into two categories, gradual changes and abrupt changes. Many researchers have attempted to propose a method to discover communities in dynamic social networks with various changes more accurately. Most of them have assumed that changes in dynamic social networks occur gradually. This presumption for the dynamic social network in which abrupt changes may occur misleads the problem. Few methods have tried to detect abrupt changes, but they used the statistical approach which has such disadvantages as the need for a lot of snapshots. In this paper, we propose a novel method to detect the type of changes using the least information of social networks and then, apply it to a new community detection framework named change-aware model. The experimental results on different benchmark and real-life datasets confirmed that the new method and framework have improved the performance of community detection algorithms.
Similar content being viewed by others
References
Bott E, Spillius EB (2014) Family and social network: Roles, norms and external relationships in ordinary urban families. Routledge
Hoffman DL, Fodor M (2010) Can you measure the ROI of your social media marketing? Sloan Manage Rev 52(1)
Jackson MO (2008) Social and economic networks. Princeton University Press, Princeton
Taylor RW, Fritsch EJ, Liederbach J (2014) Digital crime and digital terrorism. Prentice Hall Press
Fogel J, Nehmad E (2009) Internet social network communities: Risk taking, trust, and privacy concerns. Comput Human Behav 25(1):153–160
McClurg SD (2003) Social networks and political participation: The role of social interaction in explaining political participation. Polit Res Q 56(4):449–464
Tambayong L (2014) Change detection in dynamic political networks: the case of Sudan. Theories and Simulations of Complex Social Systems, Springer
Kim Y, Sohn D, Choi SM (2011) Cultural difference in motivations for using social network sites: A comparative study of American and Korean college students. Comput Human Behav 27(1):365–372
Hajibagheri A, Alvari H, Hamzeh A, Hashemi S (2012) Social networks community detection using the shapley value. In: 16th CSI international symposium on artificial intelligence and signal processing (AISP). IEEE 2012, pp 222–227
Tabarzad MA, Hamzeh A (2016) A heuristic local community detection method (HLCD). Appl Intell 2016:1–7
Lü L, Zhou T (2011) Link prediction in complex networks: A survey. Physica A 390(6):1150–1170
Ibrahim NM, Chen L (2015) Link prediction in dynamic social networks by integrating different types of information. Appl Intell 42(4):738–750
Hajibagheri A, Hamzeh A, Sukthankar G (2013) Modeling information diffusion and community membership using stochastic optimization. In: 2013 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM). IEEE, pp 175–182
Khor K-C, Ting C-Y, Phon-Amnuaisuk S (2012) A cascaded classifier approach for improving detection rates on rare attack categories in network intrusion detection. Appl Intell 36(2): 320–329
Anagnostopoulos A, Becchetti L, Castillo C, Gionis A, Leonardi S (2012) Online team formation in social networks. In: Proceedings of the 21st international conference on World Wide Web. ACM, pp 839–848
Li M, Xiang Y, Zhang B, Huang Z, Zhang J (2016) A trust evaluation scheme for complex links in a social network: A link strength perspective. Appl Intel 44(4):969–987
Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99(12):7821–7826
Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69 (2):26113
Clauset A, Newman MEJ, Moore C (2004) Finding community structure in very large networks. Phys Rev E 70(6):66111
Pizzuti C (2008) GA-Net: A genetic algorithm for community detection in social networks. Parallel Problem Solving from Nature–PPSN X, Springer
Gong M, Ma L, Zhang Q, Jiao L (2012) Community detection in networks by using multiobjective evolutionary algorithm with decomposition. Physica A 391(15):4050–4060
Hashemi S, Hamzeh A (2011) Detecting overlapping communities in social networks by game theory and structural equivalence concept. In: International conference on artificial intelligence and computational intelligence. Springer, pp 620–630
Alvari H, Hashemi S, Hamzeh A (2013) Discovering overlapping communities in social networks: A novel game-theoretic approach. AI Commun 26(2):161–177
Jalili S, Hamzeh A (2013) Enhanced overlapping community detection in social networks using wise initialization. In: 2013 5th conference on information and knowledge technology (IKT). IEEE, pp 463–466
Folino F, Pizzuti C (2014) An evolutionary multiobjective approach for community discovery in dynamic networks. Knowl Data Eng IEEE Trans 26(8):1838–1852
Ma J, Liu J, Ma W, Gong M, Jiao L (2014) Decomposition-based multiobjective evolutionary algorithm for community detection in dynamic social networks. Sci World J 2014
Kautz H, Selman B, Shah M (1997) Referral Web: combining social networks and collaborative filtering. Commun ACM 40(3):63–65
Takaffoli M, Rabbany R, Zaïane OR (2013) Incremental local community identification in dynamic social networks. In: Proceedings of the 2013 IEEE/ACM international conference on advances in social networks analysis and mining. ACM , pp 90–94
Takaffoli M, Sangi F, Fagnan J, Zaïane OR (2010) A framework for analyzing dynamic social networks. Appl Soc Netw Anal
Takaffoli M, Fagnan J, Sangi F, Zaïane OR (2011) Tracking changes in dynamic information networks. In: 2011 international conference on computational aspects of social networks (CASoN). IEEE, pp 94–101
Takaffoli M, Sangi F, Fagnan J, Zaiane OR (2011) MODEC—Modeling and detecting evolutions of communities. In: 5th international AAAI conference on weblogs and social media
Cai Q, Gong M, Ma L, Jiao L (2014) A novel clonal selection algorithm for community detection in complex networks. Comput Intell, Wiley
Hopcroft J, Khan O, Kulis B, Selman B (2003) Natural communities in large linked networks. In: Proceedings of the 9th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 541–6
Radicchi F, Castellano C, Cecconi F, Loreto V, Parisi D (2004) Defining and identifying communities in networks. Proc Natl Acad Sci USA 101(9):2658–2663
Newman MEJ (2004) Fast algorithm for detecting community structure in networks. Phys Rev E 69(6):66133
Tasgin M, Herdagdelen A, Bingol H (2007) Community detection in complex networks using genetic algorithms. arXiv:07110491
Firat A, Chatterjee S, Yilmaz M (2007) Genetic clustering of social networks using random walks. Comput Stat Data Anal 51(12):6285–6294
Asur S, Parthasarathy S, Ucar D (2009) An event-based framework for characterizing the evolutionary behavior of interaction graphs. ACM Trans Knowl Discov Data 3(4):16
Wang Y, Wu B, Du N (2008) Community evolution of social network: feature, algorithm and model. arXiv:08044356
De Meo P, Ferrara E, Fiumara G, Provetti A (2013) Enhancing community detection using a network weighting strategy. Inf Sci (Ny) 222:648–668
Sun Y, Tang J, Han J, Gupta M, Zhao B (2010) Community evolution detection in dynamic heterogeneous information networks. In: Proceedings of the 8th workshop on mining and learning with graphs. ACM, pp 137–146
Chakrabarti D, Kumar R, Tomkins A (2006) Evolutionary clustering. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 554–560
Lin Y-R, Chi Y, Zhu S, Sundaram H, Tseng BL (2008) Facetnet: a framework for analyzing communities and their evolutions in dynamic networks. In: Proceedings of the 17th international conference on World Wide Web. ACM, pp 685– 694
Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech: Theory Exp 2008(10):P10008
Chong WH, Teow LN (2013) An incremental batch technique for community detection. In: 2013 16th international conference on information fusion (FUSION), 2013 Jul 9. IEEE, pp 750–757
Nguyen NP, Dinh TN, Xuan Y, Thai MT (2011) Adaptive algorithms for detecting community structure in dynamic social networks. In: INFOCOM, 2011 Proceedings IEEE, 2011 Apr 10. IEEE, pp 2282–2290
Shang J, Liu L, Li X, Xie F, Wu C (2016) Targeted revision: A learning-based approach for incremental community detection in dynamic networks. Physica A 443:70–85
Samie ME, Hamzeh A (2016) Community detection in dynamic social networks: A local evolutionary approach. J Inf Sci, SAGE Publications. 0165551516657717
Hamming RW (1950) Error detecting and error correcting codes. Bell Syst Tech J 29(2):147–160
Wasserman S, Faust K (1994) Social network analysis: Methods and applications. Cambridge University Press
McCulloh IA, Carley KM (2008) Social network change detection. DTIC Document
Coleman TF, Moré JJ (1983) Estimation of sparse Jacobian matrices and graph coloring blems. SIAM J Numer Anal 20(1):187–209
Freeman LC (1977) A set of measures of centrality based on betweenness. Sociometry. JSTOR
Freeman LC, Roeder D, Mulholland RR (1979) Centrality in social networks: II. Experimental results. Soc Networks 2(2):119–141
McCulloh I, Carley KM (2008) Dynamic network change detection. DTIC Document
McCulloh I, Carley KM (2011) Detecting change in longitudinal social networks. DTIC Document
Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press
Fiedler M (1975) A property of eigenvectors of nonnegative symmetric matrices and its application to graph theory. Czechoslov Math J Institute Math 25(4):619–633
Danon L, Diaz-Guilera A, Duch J, Arenas A (2005) Comparing community structure identification. J Stat Mech Theory Exp 2005(9):P09008
Folino F, Pizzuti C (2010) A multiobjective and evolutionary clustering method for dynamic networks. In: 2010 international conference on advances in social networks analysis and mining (ASONAM). IEEE, pp 256–263
Lancichinetti A, Fortunato S, Radicchi F (2008) Benchmark graphs for testing community detection algorithms. Phys Rev E 78(4):46110
Kuang Q, Zhao L (2009) A practical GPU based kNN algorithm. In: International symposium on computer science and computational technology (ISCSCT) 2009 Dec, pp 151–155
Shen Z, Chen R, Andrews JG, Heath RW, Evans BL (2006) Low complexity user selection algorithms for multiuser MIMO systems with block diagonalization. IEEE Trans Signal Process 54(9):3658–3663
Le Gall F (2014) Powers of tensors and fast matrix multiplication. In: Proceedings of the 39th international symposium on symbolic and algebraic computation 2014 Jul 23. ACM, pp 296–303
Lin YR, Chi Y, Zhu S et al (2009) Analyzing communities and their evolutions in dynamic social networks. ACM Trans Knowl Discov Data (TKDD) 3(2):8
Acknowledgements
We wish to thank Dr. Clara Pizzuti and Dr. Jiaxing Shang for providing us the synthetic dataset generators and real-life datasets respectively.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Funding
This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.
Rights and permissions
About this article
Cite this article
Samie, M.E., Hamzeh, A. Change-aware community detection approach for dynamic social networks. Appl Intell 48, 78–96 (2018). https://doi.org/10.1007/s10489-017-0934-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-017-0934-z