Abstract
We propose a method for recognition of most important subgraphs in collaboration networks. The networks can be described by bipartite graphs, where basic elements, named actors, are taking part in events, organizations or activities, named acts. It is suggested that the subgraphs can be described by so-called k-cliques, which are defined as complete subgraphs of two or more vertices. The k-clique act degree is defined as the number of acts, in which a k-clique takes part. The k-clique act degree distribution in collaboration networks is investigated via a simplified model. The analytic treatment on the model leads to a conclusion that the distribution obeys a so-called shifted power law P(q) ∝ (q + α) − γ where α and γ are constants. This is a very uneven distribution. Numerical simulations have been performed, which show that the model analytic conclusion remains qualitatively correct when the model is revised to approach the real world evolution situation. Some empirical investigation results are presented, which support the model conclusion. We consider the cliques, which take part in the largest number of acts, as the most important ones. With this understanding we are able to distinguish some most important cliques in the real world networks.
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© 2009 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Fu, CH. et al. (2009). Recognition of Important Subgraphs in Collaboration Networks. In: Zhou, J. (eds) Complex Sciences. Complex 2009. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 4. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02466-5_18
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DOI: https://doi.org/10.1007/978-3-642-02466-5_18
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