Community Detection and Analysis in PPI Network

Community detection problem has been studied for years, but no agreement on the common definition of community has been reached till now. One topology may be of different types, unipartite or bipartite/multipartite. The previous literatures mostly proposed only one type of community. As the definition of community is understood differently, the grouping results are always applicable to some specified networks. If the definition changes, the grouping result will no longer be ``good''. This paper proposes an energy model to find communities in a PPI network and a hierarchical method to analyze relationship of vertices inside community. First, by analyzing the definition of community, energy model is proposed based on the characteristics of the PPI network types which are unipartite, bipartite or mixed type with both unipartite and bipartite. Second, energy model is used to find overlapping communities in PPI networks. Third, by finding the important vertices and the hierarchical levels inside a community. The experiment results show that the energy model is applicable to unipartite, bipartite or mixed PPI networks. Although the understanding of communities and the community results are different by using different community detection methods, the hierarchial method get the same level result based on different community results for a network. Hierarchical method inside community solves the limitation problem, makes community detection more accurately and universally. Furthermore, Finding local important vertices can control the spread of information of the whole network efficiently.

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