Seeding with Costly Network Information

Abstract

In the presence of contagion, decision makers strategize about where in a network to intervene (e.g., seeding a new product). A large literature has developed methods for approximately optimizing the choice of k seeds to cause the largest cascade of, for example, product adoption. However, it is often impractical to measure an entire social network. In “Seeding with Costly Network Information,” Eckles, Esfandiari, Mossel, and Rahimian develop and analyze algorithms for making a bounded number of queries of a social network and then selecting k seeds. They prove hardness results for this problem and provide almost tight approximation guarantees for their proposed algorithms under widely used models of contagion. One proposed algorithm is practical for both querying online social networks and structuring in-person surveys. This framework further allows reasoning about tradeoffs between spending budget on collecting more network data versus increasing the number of seeds.