Dynamic social network community structure evolution method based on incremental clustering

Abstract

The present invention discloses a dynamic social network community structure evolution method based on incremental clustering to solve the problems of community structure detection and communication evolution tracking in a large scale network. The method comprises a step of extracting the core node of a whole network to form a core sub graph, a step of running a hierarchical clustering algorithm on the core sub graph at a time t=0 to obtain the initial structure of a core community, and using an extended algorithm on the above basis to obtain the community structure of the whole network, and a step of using an incremental clustering algorithm to obtain the core community structure of the whole network at present time according to the dynamic evolution condition of an adjacent time network at a time t which is larger than 0 and extending the core community structure to obtain a whole community structure. Through introducing the core sub graph, the incremental calculation in the whole network is avoided, the processing speed is accelerated, and thus the method is suitable for the community discovery in the large scale network. In addition, through introducing the concept of a community structure shift, the large error of the community structure after long time evolution is avoided, and the accuracy of community evolution tracking is improved.

Description

technical field [0001] The present invention relates to the fields of data mining and complex network analysis, in particular to a dynamic social network community division method, specifically a dynamic social network community structure evolution method and system based on incremental clustering. Background technique [0002] With the development of information science and technology, network data in various fields is increasing exponentially, such as more and more mail networks in mail communication, telephone communication networks accumulated in telephone communication networks, protein networks in the field of biological sciences, etc. Wait. Research on these network data can help relevant personnel to analyze network characteristics and achieve the purpose of making full use of these networks. [0003] Among various types of complex networks, there are some networks that evolve with time. For example, in recent years, more and more researchers use graph theory to stu...

AI Technical Summary

Problems solved by technology

Based on the characteristic that the network changes little at adjacent moments in the dynamic network, the incremental clustering method can quickly calculate the community structure of the network by only processing the changed nodes or edges, and is used in large-scale networks, but this method will Reduce the quality of clustering

Images