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
Latency-Bounded Minimum Influential Node Selection in Social Networks
WASA '09 Proceedings of the 4th International Conference on Wireless Algorithms, Systems, and Applications
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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The influence maximization is an important problem in the field of social network. Informally it is to select few people to be activated in a social network such that their aggregated influence can make as many as possible people active. Kempe et al. gave a $(1-{1 \over e})$ -approximation algorithm for this problem in the linear threshold model and the independent cascade model. In addition, Chen et al. proved that the exact computation of the influence given a seed set is #P-hard in the linear threshold model. Both of the two models are based on randomized propagation, however such information might be obtained by surveys and data mining techniques. This will make great difference on the complexity of the problem. In this note, we study the complexity of the influence maximization problem in deterministic linear threshold model. We show that in the deterministic linear threshold model, there is no n 1驴驴 -factor polynomial time approximation for the problem unless P=NP. We also show that the exact computation of the influence given a seed set can be solved in polynomial time.