Google Research

Seeding with Partial Network Information : Hardness and Guarantees

ACM Conference on Economics and Computation (2019), pp. 421-422

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. 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.

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