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
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Finding effectors in social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Modeling Information Diffusion in Implicit Networks
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Finding influential mediators in social networks
Proceedings of the 20th international conference companion on World wide web
Sparsification of influence networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Buddy2GuessWho: a smartphone application in on-line social network platform
Proceedings of the 2013 Research in Adaptive and Convergent Systems
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This paper aims to combine the viral marketing with the idea of direct selling to for influence maximization in a social network. In direct selling, producers can sell the products directly to the consumers without having to go through a cascade of wholesalers. Through direct selling, it is possible to sell the products in a more efficient and economic manner. Motivated by this idea, we propose a target-selecting independent cascade (TIC) model, in which during influence propagation each active node can give up to attempt to influence some neighboring nodes, named victims, who could be hard to affect, and try to activate some of its friends of friends, termed destinations, who could have higher potential to increase the influence spread. Thus, the next question to ask is that given a social network and a set of seeds for influence propagation under TIC model, how to select targets (i.e., victims and destinations) for the attempts of activation during the propagation to boost of influence spread. We propose and evaluate three heuristics for the target selection. Experiments show that selecting targets based on influence probability between nodes have the highest boost of influence spread.