Abstract
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.
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References
Hanneman, R.A., Riddle, M.: Introduction to social network methods University of California, Riverside (2005), published in digital form http://faculty.ucr.edu/~hanneman/
Baumes, J., Goldberg, M., Magdon-Ismail, M., Wallace, W.: Discovering hidden Inform. (2004)
Tyler, J., Wilkinson, D., Huberman, B.: Email as spectroscopy: Automated discovery of community structure within organizations. In: Proc. 1st Intl. Conf. on Comm. and Tech. (2003)
Kretzschmar, M., Morris, M.: Measures of concurrency in networks and the spread of infectious disease. Math. Biosci. 133, 165–195 (1996)
Baumes, J., Goldberg, M., Magdon-Ismail, M., Wallace, W.: Discovering hidden groups in communication networks. In: Proc. 2nd NSF/NIJ Symp. on Intel. and Security Inform. (2004)
Berger-Wolf, T.Y., Saia, J.: A framework for analysis of dynamic social networks. In: Proc. KDD 2006, pp. 523–528 (2006)
Fortunato, S., Castellano, C.: Springer’s Encyclopedia of Complexity and System Science. Community Structure in Graphs (2008)
Chekuri, C., Goldberg, A., Karger, D., Levin, M., Stein, C.: Experimental study of minimum cut algorithms. In: Proc. 8th SAIM Syposium on Discreet Algorithm, pp. 324–333 (1997)
Wu, A.Y., et al.: Mining scale-free networks using geodesic clustering. In: Proc. KDD 2004, pp. 719–724 (2004)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004)
Newman, M.E.J.: Modularity and community structure in networks. PNAS 103(23), 8577–8582 (2006)
Moitra, A.: Approximation algorithms for multi commodity type problems with guarantees independent of the graph size. FOCS, 3–12 (2009)
Zhou, D., Councill, I., Zha, H., Lee Giles, C.: Discovering Temporal Communities from Social Network Documents. In: Proc. of ICDM 2007, pp. 745–750 (2007)
Tantipathananandh, C., Berger-Wolf, T., Kempe, D.: A Framework For Community Identification in Dynamic Social Networks. In: Proc. of KDD 2007, pp. 717–726 (2007)
Wasserman, S., Faust, K.: Social network analysis: Methods and applications. Cambridge University Press, New York (1994)
Wellman, B.: Computer networks as social networks. Science Magazine 293, 2031–2034 (2001)
The original dataset can be downloaded from William Cohen’s web page, http://www-2.cs.cmu.edu/~enron/
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Mishra, S., Nandi, G.C. (2013). Hierarchal Structure of Community and Link Analysis. In: Agrawal, A., Tripathi, R.C., Do, E.YL., Tiwari, M.D. (eds) Intelligent Interactive Technologies and Multimedia. IITM 2013. Communications in Computer and Information Science, vol 276. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37463-0_22
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DOI: https://doi.org/10.1007/978-3-642-37463-0_22
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