Hierarchal Structure of Community and Link Analysis

Discovering the hierarchy of organizational structure in a dynamic social network can unveil significant patterns which can help in network analysis. In this paper, we formulated a methodology to establish the most influential person in a temporal communication network from the perspective of frequency of interactions which works on hierarchal structure. With the help of frequency of interactions, we have calculated the individual score of each person from Page Rank algorithm. Subsequently, a graph is generated that showed the influence of each individual in the network. Rigorous experiments we performed using Enron data set to establish a fact that our proposed methodology correctly identifies the influential persons over the temporal network. We have used Enron Company’s email data set that describes how employees of company interacted with each other. We could analyze from our methodology and verify from the facts in the Company’s dataset since after bankruptcy, the result of interactions and behaviors of the individual of the network are absolutely known. Our result shows that the proposed methodology is generic and can be applied to other data sets of communication to identify influential at particular instances.

[1]  Ankur Moitra,et al.  Approximation Algorithms for Multicommodity-Type Problems with Guarantees Independent of the Graph Size , 2009, 2009 50th Annual IEEE Symposium on Foundations of Computer Science.

[2]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994 .

[3]  Malik Magdon-Ismail,et al.  Discovering Hidden Groups in Communication Networks , 2004, ISI.

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

[5]  M Kretzschmar,et al.  Measures of concurrency in networks and the spread of infectious disease. , 1996, Mathematical biosciences.

[6]  Andrew V. Goldberg,et al.  Experimental study of minimum cut algorithms , 1997, SODA '97.

[7]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[8]  Jiawei Han,et al.  Mining scale-free networks using geodesic clustering , 2004, KDD.

[9]  B. Wellman.  Computer Networks As Social Networks , 2001, Science.

[10]  Tanya Y. Berger-Wolf,et al.  A framework for community identification in dynamic social networks , 2007, KDD '07.

[11]  Claudio Castellano,et al.  Community Structure in Graphs , 2007, Encyclopedia of Complexity and Systems Science.

[12]  Bernardo A. Huberman,et al.  Email as spectroscopy: automated discovery of community structure within organizations , 2003 .

[13]  Tanya Y. Berger-Wolf,et al.  A framework for analysis of dynamic social networks , 2006, KDD '06.

[14]  Hongyuan Zha,et al.  Discovering Temporal Communities from Social Network Documents , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[15]  R. Hanneman.  Introduction to Social Network Methods , 2001 .