Mining quad closure patterns in Instagram

Quad Closure is a group of four people who are connected with each other. In this paper, we propose a new group recognition method for Instagram which are based on triadic closure method to determine groups on dynamic social networks (e.g likes and comments) between users as named Quad Closure. Social networks are not easily classified because of their complexity. We study how an open quad closure becomes close quad and the possibility of finding this process. There are a wide variety of factors having effects on this process. These factors include users' background information like active using time piece, photos tag frequency and sentimental analyze on comments. We try to create a unique model to predict formation of quad closure with all these factors and we share our experimental results on data taken from Instagram and show the success of our method on quad closure patterns.

[1]  Danah Boyd,et al.  Social Network Sites: Definition, History, and Scholarship , 2007, J. Comput. Mediat. Commun..

[2]  Peter Grindrod,et al.  Bistability through Triadic Closure , 2012, Internet Math..

[3]  Reda Alhajj,et al.  Identifying Social Communities by Frequent Pattern Mining , 2009, 2009 13th International Conference Information Visualisation.

[4]  Robert E. Kraut,et al.  Internet paradox. A social technology that reduces social involvement and psychological well-being? , 1998, The American psychologist.

[5]  Tore Opsahl.  Triadic closure in two-mode networks: Redefining the global and local clustering coefficients , 2013, Soc. Networks.

[6]  Xiaoming Fu,et al.  Mining triadic closure patterns in social networks , 2014, WWW.

[7]  S. Shen-Orr,et al.  Network motifs: simple building blocks of complex networks. , 2002, Science.

[8]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  Fatih Uzdilli,et al.  Potential and Limitations of Commercial Sentiment Detection Tools , 2013, ESSEM@AI*IA.

[10]  Gueorgi Kossinets,et al.  Empirical Analysis of an Evolving Social Network , 2006, Science.

[11]  Bernardo A. Huberman,et al.  Predicting the Future with Social Media , 2010, 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.

[12]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[13]  Ian Brissette,et al.  The role of optimism in social network development, coping, and psychological adjustment during a life transition. , 2002, Journal of personality and social psychology.

[14]  Navneet Kaur,et al.  Opinion mining and sentiment analysis , 2016, 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom).

[15]  J. M. Hammersley,et al.  Markov fields on finite graphs and lattices , 1971 .

[16]  Clara Pizzuti,et al.  Community detection in social networks with genetic algorithms , 2008, GECCO '08.

[17]  Jon M. Kleinberg,et al.  Group formation in large social networks: membership, growth, and evolution , 2006, KDD '06.

[18]  Jie Tang,et al.  Learning to predict reciprocity and triadic closure in social networks , 2013, TKDD.

[19]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[20]  Jukka-Pekka Onnela,et al.  Community Structure in Time-Dependent, Multiscale, and Multiplex Networks , 2009, Science.

[21]  Jon M. Kleinberg,et al.  Feedback effects between similarity and social influence in online communities , 2008, KDD.

[22]  Meir Fershtman,et al.  Cohesive group detection in a social network by the segregation matrix index , 1997 .

[23]  Steffen Staab,et al.  Social Networks Applied , 2005, IEEE Intell. Syst..

[24]  Theresa Wilson,et al.  Language Identification for Creating Language-Specific Twitter Collections , 2012 .