A Distance-based Method of Community Detection in Complex Networks

Abstract

One feature discovered in the study of complex networks is community structure,it means that vertices are gathered into several groups that the edges within groups are more than those between them.Detecting the community structure hidden in the networks has extensive application prospects.Recently,many approaches have been developed for finding communities,such as Gievan-Newman algorithm,Newman fast algorithm and so on.Also,there are some approaches consider both the network topology and the attributes of vertices,such as SA-cluster algorithm.However,a few studies focused on considering geographic distance between vertices.Tobler’s first low says that near things are more related than distant things.Based on this proposition,we think that the strength of interaction between vertices is concerned with geographic distance.By defining the weights of the edges in the network as the function of distance between two directly connected nodes,we modified the fast modularity maximization algorithm(CNM algorithm).The weight is defined in such a way that the closer the distance,the greater the weight.The algorithm is tested on the flight network of China.We consider the strength of interaction between two cities is related with both the connection of flights and distance.We find 10 communities in the air transport network,and the distribution of communities displays regionally.