基于中心度发现的中心社团  

Discovery of central community based on centrality

作　　者：卢鹏丽[1] 贾春旭[1] 

机构地区：[1]兰州理工大学计算机与通信学院,甘肃兰州730050

出　　处：《兰州理工大学学报》2012年第6期82-87,共6页Journal of Lanzhou University of Technology

基　　金：国家自然科学基金(61064011)

摘　　要：使用度中心度与流介数中心度相结合的方法,首先计算出节点的度中心度和流介数中心度,得出网络中的几何中心点和信息、物质或能量在网络上传输时经过路径最多的节点,并将这两个指标作为一个整体考虑,得到这两个指标相对比较大的节点,再在这些节点和其邻居节点上利用CPM社团发现算法,从而发现网络中的中心社团.此方法可以发现网络中相对"重要"的社团,对复杂网络上的传播机理、相继故障等分析都有一定的意义.随后利用该方法分析兰州市公共交通线路网络的中心社团结构,结果表明该社团在网络中的确可以起到比较重要的作用.By using combined method of degree centrality and flow-between centrality, the degree central- ity and flow-between centrality of the nodes were computed first and then, the geometric center of the net- work and the node with the most routing through it in course of transmission of information and sub- stances or energies on the network would be obtained. Taking these two indices as a whole into considera- tion, the nodes with these two indices of comparatively large magnitude were obtained. Therefore, the central community on the network could be discovered from among these and neighboring nodes by using CPM discovery algorithm of central community. By using this method, the relatively ＂important＂ commu- nity of the network could be found and this would have certain significance for analysis of the spreading mechanism on the complex network, and successive failure. Finally, the structure of central community of urban public traffic network of Lanzhou was analyzed with this method and its result indicated that the central community would play a central role in the whole network.

关 键 词：度中心度 流介数中心度 CPM算法 社团 复杂网络 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]