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Indexed by:期刊论文

Date of Publication:2015-11-01

Journal:COMPUTERS IN HUMAN BEHAVIOR

Included Journals:EI、SSCI、SCIE、Scopus

Volume:52

Page Number:184-189

ISSN No.:0747-5632

Key Words:Online social network; Bipartite directed graph; Influence measurement; Markov model

Abstract: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.