Fully-inductive link prediction with path-based graph neural network: A comparative analysis

Recently, fully-inductive link prediction in knowledge graphs (KGs) has aimed to predict missing links between unseen–unseen entities, independently completing evolving KGs. The latest literature emphasizes path-based graph neural network (GNN) methods, which combine traditional path-based methods with popular GNN methods, possessing generalization capability, interpretability, scalability, and high model capacity under the inductive setting. This paper presents the first comparative analysis of fully-inductive link prediction using path-based GNNs. First, we comprehensively review and summarize the research of six relevant models, divided into relational digraph-based models and Bellman–Ford algorithm-based models. Based on this, we conduct a comprehensive analysis of these models in terms of effectiveness and efficiency (including runtime, memory, and learning curves), and compared them with two subgraph-based models. Furthermore, we delve into the impact of factors such as message functions, aggregation functions, and negative sampling in the loss function on path-based GNNs. Finally, we provide an outlook on future research directions.