Reshared post from Bruno Gonçalves

Abstract: 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 ?nding seed sets that scales to very large networks. Our approach ?nds 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 ?nds 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 ?nd that highly clustered local neighborhoods and dense network-wide community structure together suppress the ability
of a trend to spread under the tipping model.
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Translation: We can quickly find the key influencing nodes in a network and feed them data that will adjust the overall shape of the network to suit our specifications.

Yowza.

Bruno Gonçalves originally shared this post:

Large Social Networks can be Targeted for Viral Marketing with Small Seed Sets. (arXiv:1205.4431v1 [cs.SI])

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 e…

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