Title: Evaluating stochastic seeding strategies in networks,
Abstract: 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 dramatically 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.