Influence propagation and maximization for heterogeneous social networks

Cheng-Te Li, Shou De Lin, Man Kwan Shan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

Abstract

Influence propagation and maximization is a well-studied problem in social network mining. However, most of the previous works focus only on homogeneous social networks where nodes and links are of single type. This work aims at defining information propagation for heterogeneous social networks (containing multiple types of nodes and links). We propose to consider the individual behaviors of persons to model the influence propagation. Person nodes possess different influence probabilities to activate their friends according to their interaction behaviors. The proposed model consists of two stages. First, based on the heterogeneous social network, we create a human-based influence graph where nodes are of human-type and links carry weights that represent how special the target node is to the source node. Second, we propose two entropy-based heuristics to identify the disseminators in the influence graph to maximize the influence spread. Experimental results show promising results for the proposed method. Copyright is held by the author/owner(s).

Original languageEnglish
Title of host publicationWWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion
Pages559-560
Number of pages2
DOIs
Publication statusPublished - 2012 May 21
Event21st Annual Conference on World Wide Web, WWW'12 - Lyon, France
Duration: 2012 Apr 162012 Apr 20

Publication series

NameWWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion

Other

Other21st Annual Conference on World Wide Web, WWW'12
CountryFrance
CityLyon
Period12-04-1612-04-20

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

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