A scalable heuristic for viral marketing under the tipping model

Paulo Shakarian, Sean Eyre, Damon Paulo

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


In a “tipping” model, each node in a social network, representing an individual, adopts a property or behavior if a certain number of his incoming neighbors currently exhibit the same. In viral marketing, a key problem is to select an initial “seed” set from the network such that the entire network adopts any behavior given to the seed. 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 seed sets that are several orders of magnitude smaller than the population size and outperform nodal centrality measures in most cases. In addition, our approach scales well—on a Friendster social network consisting of 5.6 million nodes and 28 million edges we found a seed set in under 3.6 h. Our experiments also indicate that our algorithm provides small seed sets even if high-degree nodes are removed. Last, we find that highly clustered local neighborhoods, together with dense network-wide community structures, suppress a trend’s ability to spread under the tipping model.

Original languageEnglish (US)
Pages (from-to)1225-1248
Number of pages24
JournalSocial Network Analysis and Mining
Issue number4
StatePublished - Jan 1 2013
Externally publishedYes


  • Social networks
  • Tipping model
  • Viral marketing

ASJC Scopus subject areas

  • Information Systems
  • Communication
  • Media Technology
  • Human-Computer Interaction
  • Computer Science Applications


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