Influence Maximization via Graph Neural Bandits

TL;DR

This paper introduces IM-GNB, a neural bandit framework utilizing graph neural networks to optimize influence maximization in diffusion networks with limited topology knowledge, effectively balancing exploration and exploitation.

Contribution

The paper proposes a novel neural bandit approach with GCNs for influence maximization, addressing unknown network structures and improving diffusion outcomes.

Findings

IM-GNB outperforms baseline methods in influence spread

Effective handling of unknown network topology

Significant improvement in diffusion campaign success

Abstract

We consider a ubiquitous scenario in the study of Influence Maximization (IM), in which there is limited knowledge about the topology of the diffusion network. We set the IM problem in a multi-round diffusion campaign, aiming to maximize the number of distinct users that are influenced. Leveraging the capability of bandit algorithms to effectively balance the objectives of exploration and exploitation, as well as the expressivity of neural networks, our study explores the application of neural bandit algorithms to the IM problem. We propose the framework IM-GNB (Influence Maximization with Graph Neural Bandits), where we provide an estimate of the users' probabilities of being influenced by influencers (also known as diffusion seeds). This initial estimate forms the basis for constructing both an exploitation graph and an exploration one. Subsequently, IM-GNB handles the exploration-exploitation tradeoff, by selecting seed nodes in real-time using Graph Convolutional Networks (GCN), in which the pre-estimated graphs are employed to refine the influencers' estimated rewards in each contextual setting. Through extensive experiments on two large real-world datasets, we demonstrate the effectiveness of IM-GNB compared with other baseline methods, significantly improving the spread outcome of such diffusion campaigns, when the underlying network is unknown.