A connectionist model-based approach to centrality discovery in social networks

RMIT Australia

A connectionist model-based approach to centrality discovery in social networks

Wang, Q, Yu, X and Zhang, X 2013, 'A connectionist model-based approach to centrality discovery in social networks' in L. Cao, H. Motoda, J. Srivastava, E. Lim, I. King, P. S. Yu, W. Nejdl, G. Xu, G. Li and Y. Zhang (ed.) Behaviour and Social Computing, Springer International Publishing, Switzerland, pp. 82-94.

Document type:	Book Chapter
Collection:	Book Chapters

Title	A connectionist model-based approach to centrality discovery in social networks
Author(s)	Wang, Q Yu, X Zhang, X
Year	2013
Title of book	Behaviour and Social Computing
Publisher	Springer International Publishing
Place of publication	Switzerland
Editor(s)	L. Cao, H. Motoda, J. Srivastava, E. Lim, I. King, P. S. Yu, W. Nejdl, G. Xu, G. Li and Y. Zhang
Start page	82
End page	94
Subjects	Pattern Recognition and Data Mining
Summary	Identifying key nodes in networks, in terms of centrality measurement, is one of the popular research topics in network analysis. Various methods have been proposed with different interpretations of centrality. This paper proposes a novel connectionist method which measures node centrality for directed and weighted networks. The method employs a spreading activation mechanism in order to measure the influence of a given node on the others, within an information diffusion circumstance. The experimental results show that, compared with other popular centrality measurement methods, the proposed method performs the best for finding the most influential nodes.
Copyright notice	© Springer International Publishing Switzerland 2013
ISBN	9783319040479

Versions
Version		Filter Type

Access Statistics:	208 Abstract Views - Detailed Statistics
Created:	Wed, 14 Jan 2015, 13:53:00 EST by Catalyst Administrator

Research Repository

A connectionist model-based approach to centrality discovery in social networks