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

TL;DR

This paper presents a scalable method for identifying small seed sets in large social networks that guarantee viral spread under a tipping model, with experimental validation on real-world networks.

Contribution

Introduces a fast, scalable algorithm for finding minimal seed sets that ensure complete adoption in large social networks under the tipping model.

Findings

Seed sets are often much smaller than the network size.

The method scales to networks with millions of nodes.

Network clustering and community structure inhibit trend spreading.

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 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.