摘要
We abstract the bus transport networks(BTNs)to two kinds of complex networks with space L and spaceP methods respectively.Using improved community detecting algorithm(PKM agglomerative algorithm),we analyzethe community property of two kinds of BTNs graphs.The results show that the BTNs graph described with space Lmethod have obvious community property,but the other kind of BTNs graph described with space P method have not.The reason is that the BTNs graph described with space P method have the intense overlapping community propertyand general community division algorithms can not identify this kind of community structure.To overcome this problem,we propose a novel community structure called N-depth community and present a corresponding community detectingalgorithm,which can detect overlapping community.Applying the novel community structure and detecting algorithmto a BTN evolution model described with space P,whose network property agrees well with real BTNs',we get obviouscommunity property.
We abstract the bus transport networks （BTNs） to two kinds of complex networks with space L and space P methods respectively. Using improved community detecting algorithm （PKM agglomerative algorithm）, we analyze the community property of two kinds of BTNs graphs. The results show that the BTNs graph described with space L method have obvious community property, but the other kind of BTNs graph described with space P method have not. The reason is that the BTNs graph described with space P method have the intense overlapping community property and general community division algorithms can not identify this kind of community structure. To overcome this problem, we propose a novel community structure called N-depth community and present a corresponding community detecting algorithm, which can detect overlapping community. Applying the novel community structure and detecting algorithm to a BTN evolution model described with space P, whose network property agrees well with real BTNs＇, we get obvious community property.
基金
Supported by the National Natural Science Foundation of China under Grant Nos.60504027 and 60874080
the China Postdoctoral Science Foundation Funded Project under Grant No.20060401037
