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.

翻译：本研究探讨影响力最大化问题中一个普遍存在的场景：对扩散网络的拓扑结构认知有限。我们将影响力最大化问题置于多轮扩散活动中，旨在最大化被影响的独立用户数量。借助赌博机算法有效平衡探索与利用目标的能力，以及神经网络的表达能力，本研究探索了神经赌博机算法在影响力最大化问题中的应用。我们提出了IM-GNB框架，该框架首先预估用户被影响者（亦称扩散种子）影响的概率。这一初始估计构成了构建利用图和探索图的基础。随后，IM-GNB通过图卷积网络实时选择种子节点来处理探索-利用权衡问题，其中预估计图被用于在每种情境设置下优化影响者的预估收益。通过在两个大型真实数据集上的广泛实验，我们证明了IM-GNB相较于其他基线方法的有效性，在底层网络未知的情况下显著提升了此类扩散活动的传播效果。