PageRank Bandits for Link Prediction

TL;DR

This paper introduces PageRank Bandits, a novel algorithm combining contextual bandits with PageRank to improve link prediction in graphs, addressing the exploration-exploitation dilemma and adapting to changing data.

Contribution

It presents the first fusion of PageRank with contextual bandits for link prediction, including a new reward formulation and theoretical performance guarantees.

Findings

PRB outperforms existing bandit and graph-based methods in experiments.

Theoretical guarantees support the effectiveness of PRB.

Empirical results show PRB adapts well to dynamic environments.

Abstract

Link prediction is a critical problem in graph learning with broad applications such as recommender systems and knowledge graph completion. Numerous research efforts have been directed at solving this problem, including approaches based on similarity metrics and Graph Neural Networks (GNN). However, most existing solutions are still rooted in conventional supervised learning, which makes it challenging to adapt over time to changing customer interests and to address the inherent dilemma of exploitation versus exploration in link prediction. To tackle these challenges, this paper reformulates link prediction as a sequential decision-making process, where each link prediction interaction occurs sequentially. We propose a novel fusion algorithm, PRB (PageRank Bandits), which is the first to combine contextual bandits with PageRank for collaborative exploitation and exploration. We also introduce a new reward formulation and provide a theoretical performance guarantee for PRB. Finally, we extensively evaluate PRB in both online and offline settings, comparing it with bandit-based and graph-based methods. The empirical success of PRB demonstrates the value of the proposed fusion approach. Our code is released at https://github.com/jiaruzouu/PRB.