Measuring influence in online social network based on the user-content bipartite graph

作者：

Highlights：

• We proposed a directed weighted user-content bipartite graph model.

• Our model measures influence by combined both the structural properties of the network and the contents the users published.

• An iterative algorithm is designed to compute two scores: the users’ Influence and boards’ Reach.

• The experiments verify the model can discover most influential users and popular contents effectively.

摘要

•We proposed a directed weighted user-content bipartite graph model.•Our model measures influence by combined both the structural properties of the network and the contents the users published.•An iterative algorithm is designed to compute two scores: the users’ Influence and boards’ Reach.•The experiments verify the model can discover most influential users and popular contents effectively.

论文关键词：Online social network,Bipartite directed graph,Influence measurement,Markov model

论文评审过程：Available online 15 June 2015, Version of Record 15 June 2015.

论文官网地址：https://doi.org/10.1016/j.chb.2015.04.072