TY - GEN

T1 - Large social networks can be targeted for viral marketing with small seed sets

AU - Shakarian, Paulo

AU - Paulo, Damon

PY - 2012/12/1

Y1 - 2012/12/1

N2 - 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.

AB - 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.

KW - Social networks

KW - Viral marketing

UR - http://www.scopus.com/inward/record.url?scp=84874232892&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84874232892&partnerID=8YFLogxK

U2 - 10.1109/ASONAM.2012.11

DO - 10.1109/ASONAM.2012.11

M3 - Conference contribution

AN - SCOPUS:84874232892

SN - 9780769547992

T3 - Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012

SP - 1

EP - 8

BT - Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012

T2 - 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012

Y2 - 26 August 2012 through 29 August 2012

ER -