On the hardness of approximate reasoning
Artificial Intelligence
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Discrete Applied Mathematics
On the Approximability of Influence in Social Networks
SIAM Journal on Discrete Mathematics
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
Treewidth governs the complexity of target set selection
Discrete Optimization
Hierarchical influence maximization for advertising in multi-agent markets
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Social network intelligence analysis to combat street gang violence
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Compensatory seeding in networks with varying avaliability of nodes
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
On the approximability of positive influence dominating set in social networks
Journal of Combinatorial Optimization
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In a "tipping" model, each node in a social network, representing an individual, adopts a behavior if a certain number of his incoming neighbors previously held that property. A key problem for viral marketers is to determine an initial "seed" set in a network such that if given a property then the entire network adopts the behavior. Here we introduce a method for quickly finding seed sets that scales to very large networks. Our approach finds a set of nodes that guarantees spreading to the entire network under the tipping model. After experimentally evaluating 31 real-world networks, we found that our approach often finds such sets that are several orders of magnitude smaller than the population size. Our approach also scales well - on a Friendster social network consisting of 5.6 million nodes and 28 million edges we found a seed sets in under 3.6 hours. We also find that highly clustered local neighborhoods and dense network-wide community structure together suppress the ability of a trend to spread under the tipping model.