  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.
