Abstract
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. © Springer International Publishing Switzerland 2013.
Cite
CITATION STYLE
Wang, Q., Yu, X., & Zhang, X. (2013). A connectionist model-based approach to centrality discovery in social networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8178 LNAI, pp. 82–94). Springer Verlag. https://doi.org/10.1007/978-3-319-04048-6_8
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