Building influence for online social networks

Abstract

Online social networks have been changing people’s lives from personal profiles, social activities to the method people communicate with others and commercial advertisements. Among all those activities, personal influence is an important factor. To some extent personal influence affects the final results of almost every action. In this paper, a new influence model is proposed combining the global influence and local influence. The global influence is based on the follower relationship, which is computed with Gaussian kernel density estimation. The local influence focuses on one’s influence for the communities he joins. Recognized degree and personal capability in the community are employed for computing the local influence value. The final influence value is computed with a linear combination between the global influence and the local influence. The proposed method is evaluated with a public dataset Flickr. Results show that the proposed method can provide accurate prediction for personal influence.