Recurrent Event Network for Reasoning over Temporal Knowledge Graphs

Recently, there has been a surge of interest in learning representation of graph-structured data that are dynamically evolving. However, current dynamic graph learning methods lack a principled way in modeling temporal, multi-relational, and concurrent interactions between nodes---a limitation that is especially problematic for the task of temporal knowledge graph reasoning, where the goal is to predict unseen entity relationships (i.e., events) over time. Here we present Recurrent Event Network (RE-Net)---a novel neural architecture for modeling complex event sequences---which consists of a recurrent event encoder and a neighborhood aggregator. The event encoder employs an RNN to capture (subject, relation) or (object, relation)-specific patterns from historical, multi-relational interactions between entities. The neighborhood aggregator summarizes concurrent, multi-hop entity interactions within each time stamp. An output layer is designed for predicting forthcoming events. Extensive experiments on temporal link prediction over four public TKG datasets demonstrate the effectiveness and strength of RE-Net, especially on multi-step inference over future time stamps. Code and data are published at the https://github.com/INK-USC/RE-Net {\text{GitHub repository}}.