Mining the network value of customers
Proceedings of the seventh 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
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
Influence and correlation in social networks
Proceedings of the 14th 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
Tractable models for information diffusion in social networks
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
The role of social networks in information diffusion
Proceedings of the 21st international conference on World Wide Web
Which targets to contact first to maximize influence over social network
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Probabilistic solutions of influence propagation on social networks
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Pagerank with priors: an influence propagation perspective
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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To find the most influential nodes for viral marketing, several models have been proposed to describe the influence propagation process. Among them, the Independent Cascade (IC) Model is most widely-studied. However, under IC model, computing influence spread (i.e., the expected number of nodes that will be influenced) for each given seed set has been proved to be #P-hard. To that end, in this paper, we propose GS algorithm for quick approximation of influence spread by solving a linear system, based on the fact that propagation probabilities in real-world social networks are usually quite small. Furthermore, for better approximation, we study the structural defect problem existing in networks, and correspondingly, propose enhanced algorithms, GSbyStep and SSSbyStep, by incorporating the Maximum Influence Path heuristic. Our algorithms are evaluated by extensive experiments on four social networks. Experimental results show that our algorithms can get better approximations to the IC model than the state-of-the-arts.