2009 DiscoveringOrganizationalS 4R

• (Qiu et al., 2009) ⇒ Jiangtao Qiu, Zhangxi Lin, Changjie Tang, Shaojie Qiao. (2009). “Discovering Organizational Structure in Dynamic Social Network.” In: Proceedings of the Ninth IEEE International Conference on Data Mining (ICDM 2009). doi:10.1109/ICDM.2009.86

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Abstract

• 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.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 DiscoveringOrganizationalS 4R	Jiangtao Qiu Zhangxi Lin Changjie Tang Shaojie Qiao			Discovering Organizational Structure in Dynamic Social Network				10.1109/ICDM.2009.86