Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1145/1830483.1830701 | Genetic and Evolutionary Computation Conference |
Keywords | DocType | Citations |
network structure,simple strategy,good seeding strategy,diffusion,genetic algorithms,particular social network,empirical twitter-based network,optimal seeding budget,viral marketing,viral marketing strategy,social networks,simulation,theoretical network,business,nuanced strategy,agent-based modeling,different social network structure,network characteristic,parameter space,social network,genetic algorithm,marketing,clustering coefficient,degree distribution,management,average path length | Conference | 10 |
PageRank | References | Authors |
0.69 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Forrest Stonedahl | 1 | 71 | 6.33 |
| William Rand | 2 | 140 | 16.15 |
| Uri Wilensky | 3 | 351 | 50.36 |