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
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
On the submodularity of influence in social networks
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Optimal marketing strategies over social networks
Proceedings of the 17th international conference on World Wide Web
Which Networks are Least Susceptible to Cascading Failures?
FOCS '11 Proceedings of the 2011 IEEE 52nd Annual Symposium on Foundations of Computer Science
Selection effects in online sharing: consequences for peer adoption
Proceedings of the fourteenth ACM conference on Electronic commerce
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When individuals in a social network make decisions that depend on what others have done earlier, there is the potential for a cascade to form --- a run of behaviors that are highly correlated. In an arbitrary network, the outcome of such a cascade can depend sensitively on the order in which nodes make their decisions, but to do date there has been very little investigation of how this dependence works, or how to choose an order to optimize various parameters of the cascade. Here we formulate the problem of ordering the nodes in a cascade to maximize the expected number of "favorable" decisions --- those that support a given option. We provide an algorithm that ensures an expected linear number of favorable decisions in any graph, and we show that the performance bounds for our algorithm are essentially the best achievable assuming P ≠ NP.