Identifying the optimal set of individuals to first receive information ('seeds') in a social network is a widely-studied question in many settings, such as the diffusion of information, microfinance programs, and new technologies. Numerous studies have proposed various network-centrality based heuristics to choose seeds in a way that is likely to boost diffusion. Here we show that, for some frequently studied diffusion processes, randomly seeding s plus x individuals can prompt a larger cascade than optimally targeting the best s individuals, for a small x. We prove our results for large classes of random networks, but also show that they hold in simulations over several real-world networks. This suggests that the returns to collecting and analyzing network information to identify the optimal seeds may not be economically significant. Given these findings, practitioners interested in communicating a message to a large number of people may wish to compare the cost of network-based targeting to that of slightly expanding initial outreach.
{"title":"Just a Few Seeds More: Value of Network Information for Diffusion","authors":"M. Akbarpour, Suraj Malladi, A. Saberi","doi":"10.2139/ssrn.3062830","DOIUrl":"https://doi.org/10.2139/ssrn.3062830","url":null,"abstract":"Identifying the optimal set of individuals to first receive information ('seeds') in a social network is a widely-studied question in many settings, such as the diffusion of information, microfinance programs, and new technologies. Numerous studies have proposed various network-centrality based heuristics to choose seeds in a way that is likely to boost diffusion. Here we show that, for some frequently studied diffusion processes, randomly seeding s plus x individuals can prompt a larger cascade than optimally targeting the best s individuals, for a small x. We prove our results for large classes of random networks, but also show that they hold in simulations over several real-world networks. This suggests that the returns to collecting and analyzing network information to identify the optimal seeds may not be economically significant. Given these findings, practitioners interested in communicating a message to a large number of people may wish to compare the cost of network-based targeting to that of slightly expanding initial outreach.","PeriodicalId":159122,"journal":{"name":"ORG: Adoption","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115256614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Innovations often spread by the communication of information among potential adopters. In the marketing literature, the standard model of new product diffusion is generated by information contagion: agents adopt once they hear about the existence of the product from someone else. In social learning models, by contrast, an agent adopts only when the perceived advantage of the innovation - as revealed by the actions and experiences of prior adopters - exceeds a threshold determined by the agent's prior beliefs. We demonstrate that learning with heterogeneous priors generates adoption curves that have an analytically tractable, closed-form solution. Moreover there is a simple statistical test that discriminates between this type of process and a contagion model. Applied to Griliches' classic results on the adoption of hybrid corn, this test shows that learning with heterogeneous priors does a considerably better job of explaining the data than does the contagion model.
{"title":"The Spread of Innovations Through Social Learning","authors":"H. Young","doi":"10.2139/ssrn.1024819","DOIUrl":"https://doi.org/10.2139/ssrn.1024819","url":null,"abstract":"Innovations often spread by the communication of information among potential adopters. In the marketing literature, the standard model of new product diffusion is generated by information contagion: agents adopt once they hear about the existence of the product from someone else. In social learning models, by contrast, an agent adopts only when the perceived advantage of the innovation - as revealed by the actions and experiences of prior adopters - exceeds a threshold determined by the agent's prior beliefs. We demonstrate that learning with heterogeneous priors generates adoption curves that have an analytically tractable, closed-form solution. Moreover there is a simple statistical test that discriminates between this type of process and a contagion model. Applied to Griliches' classic results on the adoption of hybrid corn, this test shows that learning with heterogeneous priors does a considerably better job of explaining the data than does the contagion model.","PeriodicalId":159122,"journal":{"name":"ORG: Adoption","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114837546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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