Communities in social networks are groups of individuals who are connected with specific goals. Discovering information on the structure, members and types of changes of communities have always been of great interest. Despite the extensive global researches conducted on these, discovery has not been confirmed yet and researchers try to find methods and improve estimated techniques by using Data Mining tools, Graph Mining tools and artificial intelligence techniques. This paper proposes a novel two-phase approach based on global and local information to detect communities in social network. It explores the global information in the first phase and then exploits the local information in the second phase to discover communities more accurately. It also proposes a novel algorithm which exploits the local information and mines deeply for the second phase. Experimental results show that the proposed method has better performance and achieves more accurate results compared with the previous ones.
JacksonMO.
Social and economic networks. 3th ed. Princeton, NJ: University Press Princeton, 2008.
2.
HoffmanDLFodorM.
Can you measure the ROI of your social media marketing?MIT Sloan Management Review2010; 52(1): 41.
3.
TaylorRWFritschEJLiederbachJ.
Digital crime and digital terrorism. 3rd ed. Upper Saddle River, NJ: Prentice Hall Press, 2014.
4.
FogelJNehmadE.
Internet social network communities: Risk taking, trust, and privacy concerns. Computers in Human Behavior2009; 25(1): 153—160.
5.
BottESpilliusEB.
Family and social network: Roles, norms and external relationships in ordinary urban families. 2nd ed. Oxon: Routledge, 2014.
6.
PowellDR.
Social network and demographic predictors of length of participation in a parent education program. Journal of Community Psychology1984; 12(1): 13—20.
7.
McClurgSD.
Social networks and political participation: The role of social interaction in explaining political participation. Political Research Quarterly2003; 56(4): 449—464.
8.
GirvanMNewmanMEJ. Community structure in social and biological networks. Proceedings of the National Academy of Sciences2002; 99(12): 7821—7826.
9.
FolinoFPizzutiC.
A multiobjective and evolutionary clustering method for dynamic networks. International Conference on Advances in Social Networks Analysis and Mining (ASONAM)2010; 2010: 256—263.
10.
NewmanMEJGirvanM. Finding and evaluating community structure in networks. Physical Review E2004; 69(2): 26113.
11.
PizzutiC.
GA-Net: A genetic algorithm for community detection in social networks. Parallel Problem Solving from Nature2008; 2008: 1081—1090.
12.
GongMMaLZhangQet al. Community detection in networks by using multiobjective evolutionary algorithm with decomposition. Physica A: Statistical Mechanics and its Applications2012; 391(15): 4050—4060.
13.
CaiQGongMMaLet al. A novel clonal selection algorithm for community detection in complex networks. Computational Intelligence2015; 31(3): 442—464.
14.
KautzHSelmanBShahM.
Referral Web: combining social networks and collaborative filtering. Communications of the ACM1997; 40(3): 63—65.
15.
TakaffoliMRabbanyRZaïaneOR.
Incremental local community identification in dynamic social networks. Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining2013; 2013: 90—94.
16.
TakaffoliMSangiFFagnanJet al. A framework for analyzing dynamic social networks. Applications of Social Network Analysis (ASNA)2010; 2010: 1—14.
17.
TakaffoliMFagnanJSangiFet al. Tracking changes in dynamic information networks. Computational Aspects of Social Networks (CASoN)2011; 2011: 94—101.
18.
TakaffoliMSangiFFagnanJet al. MODEC—Modeling and detecting evolutions of communities. Fifth International AAAI Conference on Weblogs and Social Media2011; 2011: 30—41.
19.
MaJLiuJMaWet al. Decomposition-based multiobjective evolutionary algorithm for community detection in dynamic social networks. Scientific World Journal2014; 2014: 1—23.
20.
GongMFuBJiaoLet al. Memetic algorithm for community detection in networks. Physical Review E2011; 84(5): 056101.
21.
ChenJZaianeORGoebelR.
Detecting communities in large networks by iterative local expansion. Computational Aspects of Social Networks2009; 2009: 105—112.
22.
LeskovecJLangKJMahoneyM.
Empirical comparison of algorithms for network community detection. Proceedings of the 19th International Conference on World Wide Web2010; 2010: 631—640.
23.
ClausetANewmanMEJMooreC.
Finding community structure in very large networks. Physical Review E2004; 70(6): 066111.
24.
HopcroftJKhanOKulisBet al. Natural communities in large linked networks. Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining2003; 2013: 541—546.
25.
RadicchiFCastellanoCCecconiFet al. Defining and identifying communities in networks. Proceedings of the National Academy of Sciences of the United States of America2004; 101(9): 2658—2663.
26.
NewmanMEJ. The structure and function of complex networks. SIAM Review2003; 45(2): 167—256.
27.
TasginMBingolA.
Communities detection in complex networks using genetic algorithms. Proceedings of the European Conference on Complex Systems2006; 2006: 1—6.
28.
TasginMHerdagdelenABingolH.
Community detection in complex networks using genetic algorithms. arXiv2007; arXiv:0711.0491.
29.
FiratAChatterjeeSYilmazM.
Genetic clustering of social networks using random walks. Computational Statistics & Data Analysis2007; 51(12): 6285—6294.
30.
AtzmuellerMDoerfelSMitzlaffF.
Description-oriented community detection using exhaustive subgroup discovery. Information Sciences2016; 329: 965—984.
31.
PallaGBarabásiA-LVicsekT.
Quantifying social group evolution. Nature2007; 446(7136): 664—667.
32.
AsurSParthasarathySUcarD.
An event-based framework for characterizing the evolutionary behavior of interaction graphs. ACM Transactions on Knowledge Discovery from Data (TKDD)2009; 3(4):16.
33.
WangYWuBDuN.
Community evolution of social network: feature, algorithm and model. arXiv2008; arXiv0804.4356.
34.
de MeoPFerraraEFiumaraGet al. Enhancing community detection using a network weighting strategy. Information Sciences2013; 222: 648—668.
35.
SunPGGaoLYangY.
Maximizing modularity intensity for community partition and evolution. Information Sciences2013; 236: 83—92.
36.
SunYTangJHanJet al. Community evolution detection in dynamic heterogeneous information networks. Proceedings of the Eighth Workshop on Mining and Learning with Graphs2010; 1450302149: 137—146.
37.
AstonNHuW.
Community detection in dynamic social networks. Communications and Network2014; 2014;6(02), 124.
38.
ChakrabartiDKumarRTomkinsA.
Evolutionary clustering. Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining2006; 2006: 554—560.
39.
LinYRChiYZhuSet al. FacetNet: a framework for analyzing communities and their evolutions in dynamic networks. Proceedings of the 17th International Conference on World Wide Web2008, 2008: 685—694.
40.
FolinoFPizzutiC.
An evolutionary multiobjective approach for community discovery in dynamic networks. IEEE Transactions on Knowledge and Data Engineering2014; 26(8): 1838—1852.
41.
KimMSHanJ.
A particle-and-density based evolutionary clustering method for dynamic networks. Proceedings of the VLDB Endowment2009; 2(1): 622—633.
42.
LinYRChiYZhuSet al. Analysing communities and their evolutions in dynamic social networks. ACM Transactions on Knowledge Discovery from Data (TKDD)2009; 3(2): 8.
43.
TantipathananandhCBerger-WolfTY.
Finding communities in dynamic social networks. IEEE 11th International Conference on Data Mining (ICDM)2011; 2011: 1236—1241.
44.
DanonLDiaz-GuileraADuchJet al. Comparing community structure identification. Journal of Statistical Mechanics: Theory and Experiment2005; 2005(09): P09008.
45.
RousseeuwPJ.
Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics1987; 20: 53—65.
46.
ChiYSongXZhouDet al. On evolutionary spectral clustering. ACM Transactions on Knowledge Discovery from Data (TKDD)2009; 3(4): 17.
47.
EhrgottM.
Multicriteria Optimization. Lecture Notes in Economics and Mathematical Systems2005; 491.
48.
SrinivasNDebK.
Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation1994; 2(3): 221—248.
49.
ParkYSongM.
A genetic algorithm for clustering problems. Proceedings of the Third Annual Conference on Genetic Programming1998; 1998: 568—575.
50.
MazurPZmarzlowskiKOrlowskiAJ.
Genetic algorithms approach to community detection. Acta Physica Polonica-Series A General Physics2010; 117(4): 703.
