Research Article
Recognition of Important Subgraphs in Collaboration Networks
@INPROCEEDINGS{10.1007/978-3-642-02466-5_18, author={Chun-Hua Fu and Yue-Ping Zhou and Xiu-Lian Xu and Hui Chang and Ai-Xia Feng and Jian-Jun Shi and Da-Ren He}, title={Recognition of Important Subgraphs in Collaboration Networks}, proceedings={Complex Sciences. First International Conference, Complex 2009, Shanghai, China, February 23-25, 2009. Revised Papers, Part 1}, proceedings_a={COMPLEX PART 1}, year={2012}, month={5}, keywords={subgraph collaboration network bipartite graph clique shifted power law}, doi={10.1007/978-3-642-02466-5_18} }- Chun-Hua Fu
Yue-Ping Zhou
Xiu-Lian Xu
Hui Chang
Ai-Xia Feng
Jian-Jun Shi
Da-Ren He
Year: 2012
Recognition of Important Subgraphs in Collaboration Networks
COMPLEX PART 1
Springer
DOI: 10.1007/978-3-642-02466-5_18
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 -cliques, which are defined as complete subgraphs of two or more vertices. The -clique act degree is defined as the number of acts, in which a -clique takes part. The -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 () ∝ ( + ) where and 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.
