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In a "tipping" model, each node in a social network, representing anindividual, adopts a behavior if a certain number of his incoming neighborspreviously held that property. A key problem for viral marketers is todetermine an initial "seed" set in a network such that if given a property thenthe entire network adopts the behavior. Here we introduce a method for quicklyfinding seed sets that scales to very large networks. Our approach finds a setof nodes that guarantees spreading to the entire network under the tippingmodel. After experimentally evaluating 31 real-world networks, we found thatour approach often finds such sets that are several orders of magnitude smallerthan the population size. Our approach also scales well - on a Friendstersocial network consisting of 5.6 million nodes and 28 million edges we found aseed sets in under 3.6 hours. We also find that highly clustered localneighborhoods and dense network-wide community structure together suppress theability of a trend to spread under the tipping model.

Large Social Networks can be Targeted for Viral Marketing with Small Seed Sets