Link Prediction in Continuous-Time Dynamic Heterogeneous Graphs with Causality of Event Types

Abstract

Dynamic heterogeneous graphs comprise different types of events with temporal labels. In many real-world scenarios, the temporal order of different types of events possibly implies causal relationships between these event types. However, existing methods designed to model dynamic heterogeneous graphs neglect the underlying causal relationships between event types. For instance, the determination of the occurrence of a new event is misled by irrelevant historical events considering the type and could lead to performance degradation. First, this paper explicitly defines the causality of event types by the heterogeneous causality graph to utilize such causality from the perspective of the graph structure to tackle the aforementioned issue. Second, this paper proposes the event type causality based continuous-time heterogeneous attention network (ECHN) to model dynamic heterogeneous graphs. ECHN aggregates features based on the strength of different causal relationships between event types in the prediction process to utilize the causality of event types from the perspective of the modeling algorithm. The utilities of event type causality weaken the negative effect of irrelevant events. Experimental results demonstrate that ECHN outperforms state-of-the-arts in the link prediction task. The authors believe that this paper is the first study to model the causality of event types in dynamic heterogeneous graphs explicitly.