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
Prediction of Information Diffusion Probabilities for Independent Cascade Model
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Extracting influential nodes for information diffusion on a social network
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Learning influence probabilities in social networks
Proceedings of the third ACM international conference on Web search and data mining
OASNET: an optimal allocation approach to influence maximization in modular social networks
Proceedings of the 2010 ACM Symposium on Applied Computing
Inferring networks of diffusion and influence
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery 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
Community-based greedy algorithm for mining top-K influential nodes in mobile social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Selecting information diffusion models over social networks for behavioral analysis
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Scalable Influence Maximization in Social Networks under the Linear Threshold Model
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
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Previous research efforts on the influence maximization problem assume that the network model parameters are known beforehand. However, this is rarely true in real world networks. This paper deals with the situation when the network information diffusion parameters are unknown. To this end, we firstly examine the parameter sensitivity of a popular diffusion model in influence maximization, i.e., the linear threshold model, to motivate the necessity of learning the unknown model parameters. Experiments show that the influence maximization problem is sensitive to the model parameters under the linear threshold model. In the sequel, we formally define the problem of finding the model parameters for influence maximization as an active learning problem under the linear threshold model. We then propose a weighted sampling algorithm to solve this active learning problem. Extensive experimental evaluations on five popular network datasets demonstrate that the proposed weighted sampling algorithm outperforms pure random sampling in terms of both model accuracy and the proposed objective function.