Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Active Nonlinear Tests (Ants) of Complex Simulation Models
Management Science
Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Computer simulation of a living cell
Computer simulation of a living cell
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
A New Product Growth for Model Consumer Durables
Management Science
Automatic tuning of agent-based models using genetic algorithms
MABS'05 Proceedings of the 6th international conference on Multi-Agent-Based Simulation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Estimating functional agent-based models: an application to bid shading in online markets format
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
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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.