Mining topic-level influence in heterogeneous networks
Published 26 October 2010
Lu Liu, Jie Tang, Jiawei Han, Meng Jiang, Shiqiang Yang
Citations249
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TL;DR
A generative graphical model is proposed which utilizes the heterogeneous link information and the textual content associated with each node in the network to mine topic-level direct influence and a topic- level influence propagation and aggregation algorithm is proposed to derive the indirect influence between nodes.
Abstract
Influence is a complex and subtle force that governs the dynamics of social networks as well as the behaviors of involved users. Understanding influence can benefit various applications such as viral marketing, recommendation, and information retrieval. However, most existing works on social influence analysis have focused on verifying the existence of social influence. Few works systematically investigate how to mine the strength of direct and indirect influence between nodes in heterogeneous networks.
Keywords
Computer SciencePhysics and Astronomy
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