Fine-Grained Feature-Based Social Influence Evaluation in Online Social Networks
The evaluation of a user's social influence is essential for various applications in Online Social Networks (OSNs). The authors propose a fine-grained Feature-Based social Influence (FBI) evaluation model. First, they construct a user's initial social influence by exploring two essential factors, that is, the possibility of impacting others, and the importance of the user himself. Second, they design the social influence adjustment model based on the PageRank algorithm by identifying the influence contributions of friends. For the aim of fine-grained evaluation, based on a feature set which includes the related topics and user profiles, they differentiate the feature strength of users and the tie strength of user relations. They also emphasize the effects of common neighbors in conducting influence between two users.