Abstract
Understanding the propagation of influence and the concept flow over a network in general has profound theoretical and practical implications. In this paper, we propose a novel approach to ranking individual members of a real-world communication network in terms of their roles in such propagation processes. We first improve the accuracy of the centrality measures by incorporating temporal attributes. Then, we integrate weighted PageRank and centrality scores to further improve the quality of these measures. We valid these ranking measures through a study of an email archive of a W3C working group against an independent list of experts. The results show that time-sensitive Degree, time-sensitive Betweenness and the integration of the weighted PageRank and these centrality measures yield the best ranking results. Our approach partially solves the rank sink problem of PageRank by adjusting flexible jumping probabilities with Betweenness centrality scores. Finally the text analysis based on Latent Semantic Indexing extracts key concepts distributed in different time frames and explores the evolution of the discussion topics in the social network. The overall study depicts an overview of the roles of the actors and conceptual evolution in the social network. These findings are important to understand the dynamics of the social networks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Batagelj, V., Mrvar, A.: Pajek - Analysis and Visualization of Large Networks. In: Jünger, M., Mutzel, P. (eds.) Graph Drawing Software, Springer, Berlin (2003)
Bollen, J., Rodriguez, M.A., Van de Sompel, H.: Journal status. ArXiv, January 9 (2006)
Bonacich, P.: Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology 2, 113–120 (1972)
Borgatti, S.P.: Centrality and network flow. Social Networks 27, 55–77 (2005)
Brandes, U.: A Faster Algorithm for Betweenness Centrality. Journal of Mathematical Sociology 25(2), 163–177 (2001)
Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Computer Networks 30(1-7), 107–117 (1998)
Faloutsos, C., McCurley, K., Tomkins, A.: Fast discovery of connection subgraphs. In: ACM SIGKDD, pp. 118–127 (2004)
Freeman, L.C.: A set of measures of centrality based on Betweenness. Sociometry 40, 35–41 (1997)
Freeman, L.C.: Centrality in social networks: Conceptual clarification. Social Networks 1, 215–239 (1979)
Friedkin, N.E.: Theoretical foundations for centrality measures. American Journal of Sociology, 1478–1504 (1991)
Pujol, J.M., Sangüesa, R., Delgado, J.: Extracting reputation in multi agent systems by means of social network topology. In: Proceedings of the first international joint conference on Autonomous agents and multi-agent systems, pp. 467–474 (2002)
Kleinberg, J.: Authoritative sources in a hyperlinked environment. Journal of ACM 46, 604–632 (1999)
Huberman, B., Wu, F.: Finding communities in linear time: a physics approach. The European Physical Journal B - Condensed Matter 38(2), 331–338 (2004)
Newman, M.: Who is the best connected scientist? A study of scientific co-authorship networks. In: Ben-Naim, E., Frauenfelder, H., Toroczkai, Z. (eds.) Complex Networks, pp. 337–370. Springer, Heidelberg (2004)
Nobel, C., Cook, D.J.: Graph-based anomaly detection. In: ACM SIGKDD, pp. 631–636 (2003)
Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the Web. Technical report, Stanford Digital Library Technologies Project (1998)
Sabidussi, G.: The centrality index of a graph. Psychometrika 31, 581–603 (1966)
Shetty, J., Adibi, J.: Discovering important nodes through graph entropy: The case of Enron email database. In: ACM SIGKDD (2005)
Watts, D.: Six degrees: The science of a connected age. Norton, New York (2003)
White, S., Smyth, P.: Algorithms for estimating relative importance in networks. In: ACM SIGKDD, pp. 266–275 (2003)
Freeman, L.C., Borgatti, S.P., White, D.R.: Centrality in valued graphs: A measure of Betweenness based on network flow. Social Networks 13, 141–154 (1991)
Latora, V., Marchiori, M.: cond-mat/0402050
Newman, M.E.J.: A measure of Betweenness centrality based on random walks, arXiv:cond-mat/0309045 v1 (2003)
Zhu, W., Chen, C.: Storylines: Visual exploration and analysis in latent semantic spaces. International Journal of Computers and Graphics. Special Issue on Visual Analytics 31(3), 338–349 (2007)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhu, W., Chen, C., Allen, R.B. (2008). Analyzing the Propagation of Influence and Concept Evolution in Enterprise Social Networks through Centrality and Latent Semantic Analysis. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2008. Lecture Notes in Computer Science(), vol 5012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68125-0_118
Download citation
DOI: https://doi.org/10.1007/978-3-540-68125-0_118
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68124-3
Online ISBN: 978-3-540-68125-0
eBook Packages: Computer ScienceComputer Science (R0)