%0 Conference Paper %1 Stonedahl:2010:EVM:1830483.1830701 %A Stonedahl, Forrest %A Rand, William %A Wilensky, Uri %B Proceedings of the 12th annual conference on Genetic and evolutionary computation %C New York, NY, USA %D 2010 %I ACM %K marketing om09 strategies viral %P 1195--1202 %R 10.1145/1830483.1830701 %T Evolving viral marketing strategies %U http://doi.acm.org/10.1145/1830483.1830701 %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. %@ 978-1-4503-0072-8

@inproceedings{Stonedahl:2010:EVM:1830483.1830701, 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.}, acmid = {1830701}, added-at = {2011-11-02T13:17:47.000+0100}, address = {New York, NY, USA}, author = {Stonedahl, Forrest and Rand, William and Wilensky, Uri}, biburl = {https://www.bibsonomy.org/bibtex/26d750769a4ddf013052267b1e31ea2c7/griesbau}, booktitle = {Proceedings of the 12th annual conference on Genetic and evolutionary computation}, description = {Evolving viral marketing strategies}, doi = {10.1145/1830483.1830701}, interhash = {cb8de7d2a491efee6ad8d3a9e66667af}, intrahash = {6d750769a4ddf013052267b1e31ea2c7}, isbn = {978-1-4503-0072-8}, keywords = {marketing om09 strategies viral}, location = {Portland, Oregon, USA}, numpages = {8}, pages = {1195--1202}, publisher = {ACM}, series = {GECCO '10}, timestamp = {2011-11-02T13:17:47.000+0100}, title = {Evolving viral marketing strategies}, url = {http://doi.acm.org/10.1145/1830483.1830701}, year = 2010 }