Dynamic monopolies of constant size
Journal of Combinatorial Theory Series B
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
Determining the top-k nodes in social networks using the Shapley value
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Influential nodes in a diffusion model for social networks
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
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Social network often serves as a medium for the diffusion of ideas or innovations. The problem of influence maximization which was posed by Domingos and Richardson is stated as: if we can try to convince a subset of individuals to adopt a new product and the goal is to trigger a large cascade of further adoptions, which set of individuals should we target in order to achieve a maximized influence? In this work, we proposed a potential-based node selection strategy to solve this problem. Our work is based on the observation that local most-influential node-selection adopted in many works, which is very costly, does not always lead to better result. In particular, we investigate on how to set two parameters(*** v and b uv ) appropriately. We conduct thorough experiments to evaluate effectiveness and efficiency of the proposed algorithm. Experimental results demonstrate that our approximation algorithm significantly outperforms local-optimal greedy strategy.