text: 281,15,1045009636175335425,759249,Dean Eckles,"Want to ""seed"" a behavior in a network without observing the network? Our new paper studies how to evaluate stochastic seeding strategies, such as taking ""one-hop"" from random starting nodes. <LINK> @ajwchin @jugander <LINK> The one-hop seeding strategy is designed to exploit a version of the friendship paradox (your friends have more friends than you do). It puts probability on many different seed sets, but more probability on seed sets with higher normalized in-degree. <LINK> Experiments studying this strategy have randomized villages to targeting with one-hop or (uniform) random seeding. But because these strategies are stochastic, the random selected seeds can have higher in-degree than the one-hop seeds! Here in 3/8 cases in <LINK> <LINK> The estimators we propose exploit that we know the probability of some seed set under one-hop seeding. They can dramatically increase precision and power compared with a simple difference-in-means. <LINK> You can also use these methods ""off-policy"" with existing field experiments that measure a network and randomize a few nodes to treatment. Our results so far are cautionary: one-step seeding does not seem to outperform random seeding, and might even be less effective. <LINK> One exciting this about this work is making novel reuse of data from ambitious field experiments — enabled by public data from Cai et al. <LINK> and data sharing by @betsylevyp et al. <LINK> Know any other experiments we could apply this to?",https://arxiv.org/abs/1809.09561,"When trying to maximize the adoption of a behavior in a population connected by a social network, it is common to strategize about where in the network to seed the behavior, often with an element of randomness. Selecting seeds uniformly at random is a basic but compelling strategy in that it distributes seeds broadly throughout the network. A more sophisticated stochastic strategy, one-hop targeting, is to select random network neighbors of random individuals; this exploits a version of the friendship paradox, whereby the friend of a random individual is expected to have more friends than a random individual, with the hope that seeding a behavior at more connected individuals leads to more adoption. Many seeding strategies have been proposed, but empirical evaluations have demanded large field experiments designed specifically for this purpose and have yielded relatively imprecise comparisons of strategies. Here we show how stochastic seeding strategies can be evaluated more efficiently in such experiments, how they can be evaluated ""off-policy"" using existing data arising from experiments designed for other purposes, and how to design more efficient experiments. In particular, we consider contrasts between stochastic seeding strategies and analyze nonparametric estimators adapted from policy evaluation and importance sampling. We use simulations on real networks to show that the proposed estimators and designs can increase precision while yielding valid inference. We then apply our proposed estimators to two field experiments, one that assigned households to an intensive marketing intervention and one that assigned students to an anti-bullying intervention. ",Evaluating stochastic seeding strategies in networks,6,"['Want to ""seed"" a behavior in a network without observing the network? Our new paper studies how to evaluate stochastic seeding strategies, such as taking ""one-hop"" from random starting nodes.\n<LINK>\n@ajwchin @jugander <LINK>', 'The one-hop seeding strategy is designed to exploit a version of the friendship paradox (your friends have more friends than you do). It puts probability on many different seed sets, but more probability on seed sets with higher normalized in-degree. https://t.co/ogOkmf6f7P', 'Experiments studying this strategy have randomized villages to targeting with one-hop or (uniform) random seeding. But because these strategies are stochastic, the random selected seeds can have higher in-degree than the one-hop seeds! Here in 3/8 cases in https://t.co/nQ3yuwoLGN https://t.co/l1YB2EQAIh', 'The estimators we propose exploit that we know the probability of some seed set under one-hop seeding. They can dramatically increase precision and power compared with a simple difference-in-means. https://t.co/vspuCznv4o', 'You can also use these methods ""off-policy"" with existing field experiments that measure a network and randomize a few nodes to treatment. Our results so far are cautionary: one-step seeding does not seem to outperform random seeding, and might even be less effective. https://t.co/N8S29cuWyI', 'One exciting this about this work is making novel reuse of data from ambitious field experiments — enabled by public data from Cai et al. https://t.co/Nc0j14Xt6e and data sharing by @betsylevyp et al. https://t.co/Q023CUd87L\nKnow any other experiments we could apply this to?']",18,09,1476