%0 Conference Paper
%B Proceedings of the 12th annual conference on Genetic and evolutionary computation
%D 2010
%T Evolving viral marketing strategies
%A Stonedahl,Forrest
%A Rand, William
%A Wilensky,Uri
%K Agent-based modeling
%K business
%K diffusion
%K Genetic algorithms
%K simulation
%K social networks
%K viral marketing
%X One method of viral marketing involves seeding certain consumers within a population to encourage faster adoption of the product throughout the entire population. However, determining how many and which consumers within a particular social network should be seeded to maximize adoption is challenging. We define a strategy space for consumer seeding by weighting a combination of network characteristics such as average path length, clustering coefficient, and degree. We measure strategy effectiveness by simulating adoption on a Bass-like agent-based model, with five different social network structures: four classic theoretical models (random, lattice, small-world, and preferential attachment) and one empirical (extracted from Twitter friendship data). To discover good seeding strategies, we have developed a new tool, called BehaviorSearch, which uses genetic algorithms to search through the parameter-space of agent-based models. This evolutionary search also provides insight into the interaction between strategies and network structure. Our results show that one simple strategy (ranking by node degree) is near-optimal for the four theoretical networks, but that a more nuanced strategy performs significantly better on the empirical Twitter-based network. We also find a correlation between the optimal seeding budget for a network, and the inequality of the degree distribution.
%B Proceedings of the 12th annual conference on Genetic and evolutionary computation
%S GECCO '10
%I ACM
%C New York, NY, USA
%P 1195 - 1202
%8 2010///
%@ 978-1-4503-0072-8
%G eng
%U http://doi.acm.org/10.1145/1830483.1830701
%R 10.1145/1830483.1830701