Studying Network Core And Community Structure Based On SNA

Social network refers to the actors and the social relationship between actors. That is to say that a social network is a set composed of a number of points (social actors) and the connection between points (the link between actors). Therefore, social network analysis is different from a single semantic analysis is to focus on the relational data analysis.This article applies to network information security around the social network analysis started in this direction. This paper studies the social network analysis based on e-mail. In this paper, extensive reading at home and abroad based on the literature presents a social network based on excavation e-mail new method for the core layer. The innovation of this paper mainly in the following two aspects:(1)In order to tap the core of complete social network layer members, this paper, mining social networks based on e-mail new method for the core layer. Using e-mail the data to build out a social network, the first delete the node is less than a certain threshold value of the node, then the use of Community Structure Mining and Analysis Center, part of the network to identify the core members of the deleted node last, come to a complete network of core layer. Experimental results show that this method can identify all of the core members of the network, but also to some extent is not easy to solve large-scale network computing problems.(2)In order to more precisely evaluate the degree of polymerization associations proposed in this paper based on the degree of community cohesion of the community structure of incremental mining algorithm. The definition of community External Web Link Counts and internal connections for the community cohesion ratio of the number of degrees, first select the initial node is defined as a society, and then compare the network each node added to the degree of community cohesion after the increment, select local community cohesion degree increments up to the fastest or the slowest node to reduce community members as a repeat selection to find the smallest node join the community until the specified number of members of the community, or find completely closed societies. Comparing the final excavated to determine what degree of community cohesion as a society, and which can be as isolated points.This paper presents an algorithm for programming applied to computer generated networks and a virtual enterprise network, experimental results show that the algorithm is efficient and practical.