Community detection for networks with unipartite and bipartite structure

doi:10.1088/1367-2630/16/9/093001

CORC > 北京大学 > 生命科学学院

	Community detection for networks with unipartite and bipartite structure
	Chang, Chang ; Tang, Chao
刊名	新物理学学报
	2014
关键词	community structure mixture network probabilistic model unipartite and bipartite structure NF-KAPPA-B SACCHAROMYCES-CEREVISIAE GENETIC INTERACTIONS CELL ORGANIZATION EXPRESSION ALGORITHM APOPTOSIS SOFTWARE ONTOLOGY
DOI	10.1088/1367-2630/16/9/093001
英文摘要	Finding community structures in networks is important in network science, technology, and applications. To date, most algorithms that aim to find community structures only focus either on unipartite or bipartite networks. A unipartite network consists of one set of nodes and a bipartite network consists of two nonoverlapping sets of nodes with only links joining the nodes in different sets. However, a third type of network exists, defined here as the mixture network. Just like a bipartite network, a mixture network also consists of two sets of nodes, but some nodes may simultaneously belong to two sets, which breaks the nonoverlapping restriction of a bipartite network. The mixture network can be considered as a general case, with unipartite and bipartite networks viewed as its limiting cases. A mixture network can represent not only all the unipartite and bipartite networks, but also a wide range of real-world networks that cannot be properly represented as either unipartite or bipartite networks in fields such as biology and social science. Based on this observation, we first propose a probabilistic model that can find modules in unipartite, bipartite, and mixture networks in a unified framework based on the link community model for a unipartite undirected network [B Ball et al (2011 Phys. Rev. E 84 036103)]. We test our algorithm on synthetic networks (both overlapping and nonoverlapping communities) and apply it to two real-world networks: a southern women bipartite network and a human transcriptional regulatory mixture network. The results suggest that our model performs well for all three types of networks, is competitive with other algorithms for unipartite or bipartite networks, and is applicable to real-world networks.; http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000342050300001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701 ; Physics, Multidisciplinary; SCI(E); EI; 2; ARTICLE; chang.connected@pku.edu.cn; tangc@pku.edu.cn; 16
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/209567]
专题	生命科学学院
推荐引用方式 GB/T 7714	Chang, Chang,Tang, Chao. Community detection for networks with unipartite and bipartite structure[J]. 新物理学学报,2014.
APA	Chang, Chang,&Tang, Chao.(2014).Community detection for networks with unipartite and bipartite structure.新物理学学报.
MLA	Chang, Chang,et al."Community detection for networks with unipartite and bipartite structure".新物理学学报 (2014).