Research On Community Discovery Method For Complex Traffic Network

Community discovery is an important research content in the field of social computing,community division of complex networks can solve the complicated predicament of network information and network structure to a certain extent,at the same time,it can effectively save the user's parsing time.In addition,with the continuous increase in the country's investment in major transportation infrastructure and the improvement of road network planning,the scale of national transportation network has become larger in recent years.With different regions as nodes,traffic lines between different regions as edges constitute a complex traffic network.Through the study of the topology and network characteristics of the complex traffic network,the traffic distribution in China's cities can be found.Community analysis of complex traffic networks can predict potential common economic circles and the most influential cities in the economic circle,It can provide decision-making information for enterprises and government departments.This paper analyzes the shortcomings of the existing discovery methods,and proposes an improved community discovery method for the unweighted network and weighted network respectively.Besides,experiments are carried out on typical real-world networks and complex traffic network data sets.The main research contents of this paper are the following aspects:Firstly,the agglomerative method based on the node similarity is a typical method of community detection.Aiming at the shortages of the existing method for calculating the node similarity,we propose a novel method based on the multi-layer node similarity,which not only can calculate the similarity between nodes more efficiently,but also can solve the problem of merging nodes when the node similarity is the same.Furthermore,we construct the community detection model based on the improved calculation method of the node similarity and the measure criteria of connection tightness between groups.Secondly,the connection strength between nodes in complex networks can largely affect the community structure of the network,therefore,it is of great significance to use the weight to describe the difference of the connection strength and apply it to the community discovery research.Inspired by the research of node similarity of multi-layer nodes in unweighted networks,we proposes an improved method for measuring the correlation degree of nodes based on the direct link weights of nodes and the edge weights of common neighbor nodes.Furthermore,we construct a community discovery model based on the improved measure of the correlation degree between nodes and the aggregation method between groups.Thirdly,we used community discovery based on multi-node similarity,and conducted community detection experiments on several simple real-world networks and author collaboration networks.Compared with the experimental results of Several classical methods of community discovery,the model can be more accurate to find the members of each community.Besides,based on the proposed community discovery method for complex weighted network,community discovery experiments were carried out on the weighted scientists collaboration network and the national train traffic network data,respectively.Finally,the experimental results are analyzed,which show the effectiveness of this method.