Using complex network features for fast clustering in the web

Authors:
Jintao Tang;Ting Wang;Ji Wang;Qin Lu;Wenjie Li
Affiliations:
School of Computer, National University of Defense Technology, Changsha, China;School of Computer, National University of Defense Technology, Changsha, China;National Laboratory for Parallel and Distributed Processing, Changsha, China;Hong Kong Polytechnic University, Hong Kong, Hong Kong;Hong Kong Polytechnic University, Hong Kong, Hong Kong
Venue:
Proceedings of the 20th international conference companion on World wide web
Year:
2011

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Abstract

Applying graph clustering algorithms in real world networks needs to overcome two main challenges: the lack of prior knowledge and the scalability issue. This paper proposes a novel method based on the topological features of complex networks to optimize the clustering algorithms in real-world networks. More specifically, the features are used for parameter estimation and performance optimization. The proposed method is evaluated on real-world networks extracted from the web. Experimental results show improvement both in terms of Adjusted Rand index values as well as runtime efficiency.