Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Non-monotone submodular maximization under matroid and knapsack constraints
Proceedings of the forty-first annual ACM symposium on Theory of computing
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning influence probabilities in social networks
Proceedings of the third ACM international conference on Web search and data mining
Scalable influence maximization for prevalent viral marketing in large-scale social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Social Network Analysis and Mining for Business Applications
ACM Transactions on Intelligent Systems and Technology (TIST)
Tractable models for information diffusion in social networks
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
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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%.