Quantifying Group Influence on Individuals in Online Social Networks According to social psychology studies, social networks are strongly subject to group influence; that is, usersxe2x80x99 behaviors or sentiment are primarily influenced by their group environments. But only a few studies to date have examined group influence in online social networks (OSNs). In this paper, a Factor Graphbased Group Influence model (F2GI) is proposed to depict the influence of perceived groups on a user. First, we analyze individualsxe2x80x99 group environments and propose definitions and detecting methods of two types of groups: following groups and interacting groups. Secondly, based on these groups, we quantity the groupsxe2x80x99 features and propose the F2GI model able to describe group influence. Finally, using the retweet behaviors as the primarily manifestations of group influence, we apply our model to predict individualsxe2x80x99 retweet behaviors and to validate how much influence the group puts on individuals. The results show that our group influence model can depict group influence more effectively and predict the actions of users who are affected by perceived groups accurately with social psychology ideas than classical machine learning methods. We believe that, moreover, our model can also be applied to marketing strategies, advertisement strategies and the prediction of public opinion.

IIRank: A Novel Algorithm for Identifying Influencers in MicroBlog Social Networks In this paper, we analyze the influence of microblogger and put forward new concepts of sources, such as the positive microblog source, the negative microblog source, and the neutral source in microblog field based on the concepts of sources in electromagnetic field theory. Then, we propose a novel Individual Influence Ranking algorithm called IIRank. IIRank first calculates the microblog flux by the way of calculating the magnetic flux to assess usersu0027 behaviors. It then calculates the microblog flux density with the microblog flux to rank the microbloggeru0027s influence. We have implemented the algorithm to handle the realworld Sina Weibo data. Experimental results show that the proposed method significantly improves the assessment preciseness over the popular and famous PageRank algorithm.