Identifying Users´ Interest Similarity Based on Clustering Hot Vertices in Social Networks

Identifying users´ similarity is a very important researching point because its result can be applied to many application systems. In social networks, the user circles are built not only based on their relationships in real-life, but also on common interests. Some existing approaches cannot fully capture users´ similarity from the perspective of their common interests, while some other approaches are too time-consuming or space-consuming. In this paper, we propose a method of identifying users´ interest similarity based on clustering Hot Vertices (HotV). A hot vertex in a social network is an account which has a large number of fans. The approach extracts users´ common interests by mining and clustering the hot vertices that the two users are following simultaneously. Both the experiment and theoretical analysis have proved that the proposed approach makes a significant improvement on the precision of similarity measuring with a relatively low time and space complexity.