Leveraging Social Network Analysis with Topic Models and the Semantic Web
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
A new blockmodeling based hierarchical clustering algorithm for web social networks
Engineering Applications of Artificial Intelligence
Leveraging social network analysis with topic models and the Semantic Web extended
Web Intelligence and Agent Systems - Web Intelligence and Communities
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Applying the concept of organizational structure to social network analysis may well represent the power of members and the scope of their power in a social network. In this paper, we propose a data structure, called Community Tree, to represent the organizational structure in the social network. We combine the PageRank algorithm and random walks on graph to derive the community tree from the social network. In the real world, a social network is constantly changing. Hence, the organizational structure in the social network is also constantly changing. In order to present the organizational structure in a dynamic social network, we propose a tree learning algorithm to derive an evolving community tree. The evolving community tree enables a smooth transition between the two community trees and well represents the evolution of organizational structure in the dynamic social network. Experiments conducted on real data show our methods are effective at discovering the organizational structure and representing the evolution of organizational structure in a dynamic social network.