A Link Prediction Model of Dynamic Heterogeneous Network Based on Transformer

Abstract

It has always been a challenge to research inductive learning, which can embed newly unseen nodes. Inductive learning is a frequently encountered problem in practical applications of graph networks, but there is little research on dynamic heterogeneous network link prediction. Therefore, we propose a Heterogeneous and Temporal Model Based on Transformer (HT-Trans) for dynamic heterogeneous network, which core idea is to introduce transformer to integrate better neighbor information to capture network structure. The goal of HT-Trans is to infer proper embedding for existing nodes and unseen nodes. Experimental results show that the algorithm proposed in this paper is significantly competitive compared with baselines for link prediction tasks on three real datasets.