Integer and combinatorial optimization
Integer and combinatorial optimization
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
Information diffusion through blogspace
Proceedings of the 13th international conference on World Wide Web
Topic and role discovery in social networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Influential nodes in a diffusion model for social networks
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
Ranking User Influence in Healthcare Social Media
ACM Transactions on Intelligent Systems and Technology (TIST)
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We address the problem of maximizing the spread of information in a large-scale social network based on the Independent Cascade Model (ICM). When we solve the influence maximization problem, that is, the optimization problem of selecting the most influential nodes, we need to compute the expected number of nodes influenced by a given set of nodes. However, an exact calculation or a good estimate of this quantity needs a large amount of computation. Thus, very large computational quantities are needed to approximately solve the influence maximization problem based on a natural greedy algorithm. In this paper, we propose methods to efficiently obtain good approximate solutions for the influence maximization problem in the case where the propagation probabilities through links are small. Using real data on a large-scale blog network, we experimentally demonstrate the effectiveness of the proposed methods.