A Information Flow Centrality Based Method For Complex Networks Dynamic Community Tracking

Complex network exists in the real world widely,therefore,an in-depthunderstanding of the structure of the complex network community will help us to solvemany practical problems.Complex network not only has the small-world and scalecharacteristics, it also has the structural characteristics of the community. As time goes on,due to the complex network is evolving dynamic process, at the same time, the networkwill produce dynamic community structures,therefore, study of the evolution of thenetwork community is very important.For a better basis for the network core nodes to track the dynamic community ofnetwork, core network nodes indicators (Local Centrality)based on network localinformation flow are proposed in this paper,the experimental results show the rationalityand advantages of the indicators.A discovery algorithm of the network core nodesaccording to the LC value is also proposed in this paper, The algorithm can accurately findimportant node in each local area network,we can get the core node set of the networkaccording to the algorithm.This paper presents Online Community partitioning algorithm(CNBA) based on the core nodes,to combine the core nodes of the network andcommunity structures closely we can get more accurate results during the process oftracking the dynamic online community.In order to determine the dynamic evolution chain of the online communities,thispaper gives determination conditions of precursor-subsequent at a different time throughthe core node of network,and according to the relationship of precursor-successor of theonline community,we clearly describe the six events that may occur during the process ofthe evolution of online communities,thus,we can accurately track dynamic change of theonline communities.