community detection;
energy model;
PPI networks;
overlapping communities;
D O I:
暂无
中图分类号:
TP39 [计算机的应用];
学科分类号:
081203 ;
0835 ;
摘要:
No common definition of community has been agreed upon till now. One topology can be of different types, unipartite or bipartite/multipartite. Most of the former literatures are proposed for one type of community. As the understanding of the community definition is different, the grouping results always applicable to the specified network. If the definition changes, the grouping result will no longer be "good". To do the community detection in mixed protein-protein interaction (PPI) networks, we propose a community detection method with two steps. Firstly, group vertices "must be" in the same community by properties; secondly, find overlapping vertices by functions. We apply the energy model to find community structure in PPI networks. The results show that our method is applicable to PPI network, unipartite, bipartite or mixed. It groups vertices with similar property/roles in the same community and finds overlapping vertices in the network.
机构:
Department of Electrical and Computer Engineering, University of California, San DiegoDepartment of Electrical and Computer Engineering, University of California, San Diego
Narayanan T.
Gersten M.
论文数: 0引用数: 0
h-index: 0
机构:
Graduate Program in Bioinformatics and Systems Biology, University of California, San DiegoDepartment of Electrical and Computer Engineering, University of California, San Diego
Gersten M.
Subramaniam S.
论文数: 0引用数: 0
h-index: 0
机构:
Department of Bioengineering, University of California, San DiegoDepartment of Electrical and Computer Engineering, University of California, San Diego
Subramaniam S.
Grama A.
论文数: 0引用数: 0
h-index: 0
机构:
Department of Computer Science, Purdue University, West Lafayette, INDepartment of Electrical and Computer Engineering, University of California, San Diego