Inductive Relation Prediction Using Analogy Subgraph Embeddings

Jiarui Jin, Yangkun Wang, Kounianhua Du, Weinan Zhang, Zheng Zhang, David Wipf, Yong Yu, Quan Gan

Keywords: Link Prediction, Relation Modelling, Heterogeneous Graphs, Knowledge Graphs

Abstract: Prevailing methods for relation prediction in heterogeneous graphs aim at learning latent representations (i.e., embeddings) of observed nodes and relations, and thus are limited to the transductive setting where the relation types must be known during training. Here, we propose ANalogy SubGraphEmbeddingLearning (GraphANGEL), a novel relation prediction framework that predicts relations5between each node pair based on the subgraphs containing the pair, as well as other (analogy) subgraphs with the same graph patterns. Each graph pattern explicitly represents a specific logical rule, which contributes to an inductive bias that facilitates generalization to unseen relations and leads to more explainable predictive models. Moreover, our method also removes the limited neighborhood constraint of graph neural networks. Our model consistently outperforms existing models on heterogeneous graph based recommendation as well as knowledge graph completion. We also empirically demonstrate our model’s capability in generalizing to new relations while producing explainable heat maps of attention scores across the discovered logic.

One-sentence Summary: In this paper, we propose GraphANGEL, a novel relation prediction framework that predicts (new) relations between each node pair by checking whether the subgraphs containing the pair are similar to other subgraphs containing the considered relation.