Influence Maximization with Bandits

TL;DR

This paper addresses influence maximization by proposing a bandit-based approach that learns influence probabilities through sequential seed set trials, demonstrating effectiveness on real datasets.

Contribution

It introduces a combinatorial bandit framework for influence maximization that operates without prior influence probability data, including novel node-level feedback analysis.

Findings

Effective influence estimation via bandits on real datasets

Theoretical bounds established for influence maximization performance

Practical implementation shows efficiency and effectiveness

Abstract

We consider the problem of \emph{influence maximization}, the problem of maximizing the number of people that become aware of a product by finding the `best' set of `seed' users to expose the product to. Most prior work on this topic assumes that we know the probability of each user influencing each other user, or we have data that lets us estimate these influences. However, this information is typically not initially available or is difficult to obtain. To avoid this assumption, we adopt a combinatorial multi-armed bandit paradigm that estimates the influence probabilities as we sequentially try different seed sets. We establish bounds on the performance of this procedure under the existing edge-level feedback as well as a novel and more realistic node-level feedback. Beyond our theoretical results, we describe a practical implementation and experimentally demonstrate its efficiency and effectiveness on four real datasets.