Seeding with Costly Network Information

Abstract

We study the choice of k nodes in a social network to seed a diffusion with maximum expected spread size. Most of the previous work on this problem (known as influence maximization) focuses on efficient algorithms to approximate the optimal seed sets with provable guarantees, while assuming knowledge of the entire network. However, in practice, obtaining full knowledge of the network is very costly. To address this gap, we propose algorithms that make a bounded number of queries to the graph structure and provide almost tight approximation guarantees with matching lower bounds on the required number of queries. We test our algorithms on empirical network data to quantify the trade-off between the cost of obtaining more refined network information and the benefit of the added information for guiding improved seeding policies.

Keywords: Viral marketing, influence maximization, social networks, submodular maximization, query oracle

JEL Classification: D85, D83, O12, Z13