TREND: TempoRal Event and Node Dynamics for Graph Representation Learning

TL;DR

TREND introduces a novel inductive temporal graph representation learning framework that models event and node dynamics, capturing exciting effects with a Hawkes process-based GNN, leading to improved accuracy on real-world datasets.

Contribution

It is the first to integrate both event and node dynamics with Hawkes process modeling in an inductive GNN framework for temporal graphs.

Findings

Outperforms existing methods on four real-world datasets.

Effectively models exciting effects between events.

Captures individual and collective event characteristics.

Abstract

Temporal graph representation learning has drawn significant attention for the prevalence of temporal graphs in the real world. However, most existing works resort to taking discrete snapshots of the temporal graph, or are not inductive to deal with new nodes, or do not model the exciting effects which is the ability of events to influence the occurrence of another event. In this work, We propose TREND, a novel framework for temporal graph representation learning, driven by TempoRal Event and Node Dynamics and built upon a Hawkes process-based graph neural network (GNN). TREND presents a few major advantages: (1) it is inductive due to its GNN architecture; (2) it captures the exciting effects between events by the adoption of the Hawkes process; (3) as our main novelty, it captures the individual and collective characteristics of events by integrating both event and node dynamics, driving a more precise modeling of the temporal process. Extensive experiments on four real-world datasets demonstrate the effectiveness of our proposed model.