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Title: Influence propagation and maximization for heterogeneous social networks
Authors: Li, C.-T.;Lin, S.-D.;Shan, Man-Kwan
Contributors: 資科系
Keywords: Entropy-based;Individual behavior;Influence graph;Influence maximizations;Information propagation;Interaction behavior;Nodes and links;Social Networks;Source nodes;Target nodes;World Wide Web;Social networking (online)
Date: 2012
Issue Date: 2015-04-17 10:55:40 (UTC+8)
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).
Relation: WWW'12 - Proceedings of the 21st Annual Conference on World Wide Web Companion
Data Type: conference
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