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.*__________*

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:

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…