Quantitative Similarity Evaluation of Internet Social Network Entities Based on Supernetwork - Xiuzhen Chen; Shenghong Li; Jianhua Li; Zhiyuan Zhang (Journal of Computer Networks and Communications)
How to accurately characterize similarities of entities is the basis of detecting virtual community structure of an Internet social network. This paper proposes a supernetwork based approach of quantitative similarity evaluation among entities with two indices of friend relation and interest similarity. The supernetwork theory is firstly introduced to model the complex relationship of online social network entities by integrating three basic networks: entity, action, and interest and establishing three kinds of mappings: from entity to action, from action to interest, and from entity to interest, that is, one hidden relation mined through the transfer characteristic of visible mappings. And further similarity degree between two entities is calculated by weighting the values of two indices: friend relation and interest similarity. Experiments show that this model not only can provide a more realistic relation of individual users within an Internet social network, but also, build a weighted social network, that is, a graph in which user entities are vertices and similarities are edges, on which the values record their similarity strength relative to one another.