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论文类型：期刊论文

发表时间：2015-11-01

发表刊物：COMPUTERS IN HUMAN BEHAVIOR

收录刊物：EI、SSCI、SCIE、Scopus

卷号：52

页面范围：184-189

ISSN号：0747-5632

关键字：Online social network; Bipartite directed graph; Influence measurement; Markov model

摘要：With the rising of online social networks, influence has been a complex and subtle force to govern users' behaviors and relationship formation. Therefore, how to precisely identify and measure influence has been a hot research direction. Differentiating from existing researches, we are devoted to combining the status of users in the network and the contents generated from these users to synthetically measure the influence diffusion. In this paper, we firstly proposed a directed user-content bipartite graph model. Next, an iterative algorithm is designed to compute two scores: the users' Influence and boards' Reach. Finally, we conduct extensive experiments on the dataset extracted from the online community Pinterest. The experimental results verify our proposed model can discover most influential users and popular broads effectively and can also be expected to benefit various applications, e.g., viral marketing, personal recommendation, information retrieval, etc. (C) 2015 Elsevier Ltd. All rights reserved.