FGSN: Fuzzy Granular Social Networks - Model and applications

Abstract Social network data has been modeled with several approaches, including Sociogram and Sociomatrices, which are popular and comprehensive. Similar to these we have developed here a novel modeling technique based on granular computing theory and fuzzy neighborhood systems, which provides a uniform framework to represent social networks. In this model, a social network is represented with a collection of granules. Fuzzy sets are used for defining the granules. The model is named Fuzzy Granular Social Network (FGSN). Familiar measures of networks viz. degree, betweenness, embeddedness and clustering coefficient are redefined in the context of this new framework. Two measures, namely, entropy of FGSN and energy of granules are defined to quantify the uncertainty involved in FGSN arising from fuzziness in the relationships of actors. Experimental results demonstrate the applicability of the model in two well known problems of social networks, namely, target set selection and community detection with comparative studies.

[1]  Kathleen M. Carley,et al.  Clearing the FOG: Fuzzy, overlapping groups for social networks , 2008, Soc. Networks.

[2]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Éva Tardos,et al.  Strategic network formation with structural holes , 2008, EC '08.

[4]  M. Last.  Pattern Recognition Algorithms for Data Mining , 2007 .

[5]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[6]  Mark S Handcock,et al.  7. Respondent-Driven Sampling: An Assessment of Current Methodology , 2009, Sociological methodology.

[7]  M E J Newman,et al.  Fast algorithm for detecting community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  Jiye Liang,et al.  Information Granularity in Fuzzy Binary GrC Model , 2011, IEEE Transactions on Fuzzy Systems.

[9]  Éva Tardos,et al.  Influential Nodes in a Diffusion Model for Social Networks , 2005, ICALP.

[10]  Y. Narahari,et al.  A Shapley Value-Based Approach to Discover Influential Nodes in Social Networks , 2011, IEEE Transactions on Automation Science and Engineering.

[11]  Witold Pedrycz,et al.  Granular Computing: Analysis and Design of Intelligent Systems , 2013 .

[12]  S. Boorman,et al.  Social Structure from Multiple Networks. II. Role Structures , 1976, American Journal of Sociology.

[13]  Lotfi A. Zadeh,et al.  Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic , 1997, Fuzzy Sets Syst..

[14]  Martin Milanič,et al.  Latency-bounded target set selection in social networks , 2013, Theor. Comput. Sci..

[15]  S. Boorman,et al.  Social structure from multiple networks: I , 1976 .

[16]  Jure Leskovec,et al.  Predicting positive and negative links in online social networks , 2010, WWW '10.

[17]  C. A. Murthy,et al.  Centrality Measures, Upper Bound, and Influence Maximization in Large Scale Directed Social Networks , 2014, Fundam. Informaticae.

[18]  Vladik Kreinovich,et al.  Handbook of Granular Computing , 2008 .

[19]  Matthew Richardson,et al.  Mining knowledge-sharing sites for viral marketing , 2002, KDD.

[20]  Christos Faloutsos,et al.  Graph evolution: Densification and shrinking diameters , 2006, TKDD.

[21]  A ZadehLotfi.  Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic , 1997 .

[22]  C. Liau,et al.  Positional Analysis in Fuzzy Social Networks , 2007, IEEE International Conference on Granular Computing.

[23]  Piotr Honko,et al.  Association discovery from relational data via granular computing , 2013, Inf. Sci..

[24]  Matthew Richardson,et al.  Mining the network value of customers , 2001, KDD '01.

[25]  Young Ae Kim,et al.  A trust prediction framework in rating-based experience sharing social networks without a Web of Trust , 2012, Inf. Sci..

[26]  J. Moreno.  Who Shall Survive: A New Approach to the Problem of Human Interrelations , 2017 .

[27]  B. C. Brookes,et al.  Information Sciences , 2020, Cognitive Skills You Need for the 21st Century.

[28]  Lada A. Adamic,et al.  The political blogosphere and the 2004 U.S. election: divided they blog , 2005, LinkKDD '05.

[29]  J. Kleinberg.  Algorithmic Game Theory: Cascading Behavior in Networks: Algorithmic and Economic Issues , 2007 .

[30]  T. Vicsek,et al.  Uncovering the overlapping community structure of complex networks in nature and society , 2005, Nature.

[31]  G. J. Fleer,et al.  Stationary dynamics approach to analytical approximations for polymer coexistence curves. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[32]  Chen Lin,et al.  Personalized news recommendation via implicit social experts , 2014, Inf. Sci..

[33]  Shuting Chen,et al.  Dynamic Grade on the Major Hazards Using Community Detection Based on Genetic Algorithm , 2009, 2009 International Conference on Signal Processing Systems.

[34]  Xiuzhen Chen,et al.  A new genetics-based diffusion model for social networks , 2011, 2011 International Conference on Computational Aspects of Social Networks (CASoN).

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

[36]  Guojun Wang,et al.  κ-FuzzyTrust: Efficient trust computation for large-scale mobile social networks using a fuzzy implicit social graph , 2015, Inf. Sci..

[37]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[38]  Cheng Wang,et al.  A global optimization algorithm for target set selection problems , 2014, Inf. Sci..

[39]  Sankar K. Pal,et al.  Pattern Recognition Algorithms for Data Mining , 2004 .

[40]  Günce Keziban Orman,et al.  The Effect of Network Realism on Community Detection Algorithms , 2010, 2010 International Conference on Advances in Social Networks Analysis and Mining.

[41]  Xuhui Chen,et al.  An entropy-based uncertainty measurement approach in neighborhood systems , 2014, Inf. Sci..

[42]  Sankar K. Pal,et al.  Fuzzy–Rough Sets for Information Measures and Selection of Relevant Genes From Microarray Data , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[43]  Bin Wu,et al.  Maximizing the spread of influence ranking in social networks , 2014, Inf. Sci..

[44]  S. Boorman,et al.  Social Structure from Multiple Networks. I. Blockmodels of Roles and Positions , 1976, American Journal of Sociology.

[45]  Sankar K. Pal,et al.  Title Paper: Natural computing: A problem solving paradigm with granular information processing , 2013, Appl. Soft Comput..

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

[47]  S.T. Sarasamma,et al.  Data Mining Through Fuzzy Social Network Analysis , 2007, NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society.

[48]  Gustavo Arango.  Centrality Measures , 2014, Encyclopedia of Social Network Analysis and Mining.

[49]  W. Zachary,et al.  An Information Flow Model for Conflict and Fission in Small Groups , 1977, Journal of Anthropological Research.

[50]  D. Lusseau,et al.  The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations , 2003, Behavioral Ecology and Sociobiology.

[51]  Witold Pedrycz,et al.  Granular Computing: Perspectives and Challenges , 2013, IEEE Transactions on Cybernetics.

[52]  Soumitra Dutta,et al.  Fuzzy rough granular neural networks, fuzzy granules, and classification , 2011, Theor. Comput. Sci..

[53]  Sankar K. Pal,et al.  Granular Mining and Rough-Fuzzy Pattern Recognition: A Way to Natural Computation , 2012, IEEE Intell. Informatics Bull..

[54]  Leon Danon,et al.  Comparing community structure identification , 2005, cond-mat/0505245.

[55]  Guojun Wang,et al.  κ-FuzzyTrust , 2015 .

[56]  Sankar K. Pal,et al.  Fuzzy Rough Granular Neural Networks for Pattern Analysis , 2017 .

[57]  Manuel Cebrián,et al.  The Genetic Algorithm as a General Diffusion Model for Social Networks , 2010, AAAI.

[58]  Guoyin Jiang,et al.  Evolution of knowledge sharing behavior in social commerce: An agent-based computational approach , 2014, Inf. Sci..

[59]  M. Spreen.  Rare Populations, Hidden Populations, and Link-Tracing Designs: What and Why? , 1992 .