W-Louvain: A Group Detection Algorithm Based on Synthetic Vectors

Abstract

Most of the hidden dangers of network system security are caused by group events. Group analysis and data mining for them are of great significance to ensure network security. Although the existing group detection algorithms have achieved a series of results, they can only be divided on one of the network structure and group attributes, but cannot combine them together, which has certain limitations. The comprehensive vector can be constructed by collecting and mining the group data which cause the hidden danger of security, which can analyze the hidden danger of security from the aspects of network structure and node attribute, so as to realize the guidance and control of group behavior. Therefore, in view of the above problems, this paper proposes a group detection algorithm based on synthesis vector, which can finally find a special group which is closely connected in structure and very similar in attribute. Firstly, the comprehensive similarity is calculated based on the fusion vector in the sharing layer of the comprehensive vector computing model. Then, reconstruct the weighted network diagram. Finally, based on Louvain algorithm, the improvement is carried out. The improved algorithm is referred to as the W-Louvain algorithm. The W-Louvain algorithm is used to divide the groups, and the closely connected vectors in the structure and the very similar vectors in the attributes are divided into the same group. Experiments show that on multiple datasets the evaluation indexes of W-Louvain algorithm, such as modularity Q, number k of community, density D of community and similarity degree S of comprehensive vector attribute, are better than the existing methods.