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 de ne a
strategy space for consumer seeding by weighting a
combination of network characteristics such as average
path length, clustering coe cient, and degree. We
measure strategy e ectiveness by simulating adoption on
a Bass-like agent-based model, with ve di erent 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. pdf 2010
