Discovering Influential Nodes from Trust Network

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Conference Paper

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Publication Title

Proceedings of the 28th Annual ACM Symposium on Applied Computing

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The goal of viral marketing is that, by the virtue of mouth to mouth word spread, a small set of influential customers can influence more customers. Influence maximization (IM) task is used to discover such influential nodes (or customers) from a social network. Existing algorithms for IM adopt Greedy and Lazy forward optimization approaches which assume only positive influence among users and availability of influence probability, the probability that a user is influenced by another. In this work, we propose the T-GT model, which considers both positive (trust) and negative (distrust) influences in social trust networks. We first compute positive and negative influences by mining frequent patterns of actions performed by users. Then, a local search based algorithm called mineSeedLS for node add, exchange and delete operations, is proposed to discover influential nodes from trust networks. Experimental results shows that our approach outperforms Greedy based approach by about 35%.