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
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The dynamics of viral marketing
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Extracting influential nodes for information diffusion on a social network
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Efficient estimation of influence functions for SIS model on social networks
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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Learning Continuous-Time Information Diffusion Model for Social Behavioral Data Analysis
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Extracting influential nodes on a social network for information diffusion
Data Mining and Knowledge Discovery
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
Finding relation between PageRank and voter model
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
Scalable Influence Maximization in Social Networks under the Linear Threshold Model
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Everyone's an influencer: quantifying influence on twitter
Proceedings of the fourth ACM international conference on Web search and data mining
SBP'12 Proceedings of the 5th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Influence spread in large-scale social networks --- a belief propagation approach
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
On approximation of real-world influence spread
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
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We address a new type of influence maximization problem which we call "target selection problem". This is different from the traditionally thought influence maximization problem, which can be called "source selection problem", where the problem is to find a set of K nodes that together maximizes their influence over a social network. The very basic assumption there is that all these K nodes can be the source nodes, i.e. can be activated. In "target selection problem" we maximize the influence of a new user as a source node by selecting K nodes in the network and adding a link to each of them. We show that this is the generalization of "source selection problem" and also satisfies the submodularity. The selected nodes are substantially different from those of "source selection problem" and use of the solution of "source selection problem" results in a very poor performance.