PageRank Bandits for Link Prediction
This work addresses link prediction for applications like recommender systems and knowledge graph completion, offering a novel fusion approach that is incremental in combining existing techniques.
The paper tackles the problem of link prediction in graph learning by reformulating it as a sequential decision-making process to adapt to changing interests and balance exploitation versus exploration, proposing the PRB algorithm that combines contextual bandits with PageRank, and demonstrates empirical success in online and offline evaluations.
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.