Extending Full Transitive Closure to Rank Removable Edges in GN Algorithm

Abstract Most of the real-world networks exhibit community structure, a property that reveals the existence of natural vertex clusters whose inter-edge density is lower than intra-edge density between various groups. Despite providing a better understanding of network structure and characteristics, community detection has many practical applications in diverse domains. Communities obtained from the telephone network provides many useful information that can be used for churn prediction, budget control in organizations etc. Detecting communities is a fundamental need in the area of networks, yet challenging. In this paper, we propose an extension to the Girvan-Newman algorithm for finding the betweenness using the transitive closure property and the greedy technique in Dijkstra's single source shortest path method.

[1]  Boleslaw K. Szymanski,et al.  A New Metric for Quality of Network Community Structure , 2015, ArXiv.

[2]  Pablo M. Gleiser,et al.  Community Structure in Jazz , 2003, Adv. Complex Syst..

[3]  Jyothisha J. Nair,et al.  Towards efficient analysis of massive networks , 2015 .

[4]  Damir Vukicevic,et al.  Community structure in networks: Girvan-Newman algorithm improvement , 2014, 2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).

[5]  S. Fortunato,et al.  Resolution limit in community detection , 2006, Proceedings of the National Academy of Sciences.

[6]  Bin Wu,et al.  A Multi-source Message Passing Model to Improve the Parallelism Efficiency of Graph Mining on MapReduce , 2012, 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum.

[7]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[8]  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.

[9]  Andrei Z. Broder,et al.  Graph structure in the Web , 2000, Comput. Networks.

[10]  David Lusseau,et al.  The emergent properties of a dolphin social network , 2003, Proceedings of the Royal Society of London. Series B: Biological Sciences.

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

[12]  Boleslaw K. Szymanski,et al.  On Measuring the Quality of a Network Community Structure , 2013, 2013 International Conference on Social Computing.

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

[14]  Konstantin Avrachenkov,et al.  Cooperative Game Theory Approaches for Network Partitioning , 2017, COCOON.

[15]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.