  In this paper, we propose the Graph Temporal Edge Aggregation (GTEA)
framework for inductive learning on Temporal Interaction Graphs (TIGs).
Different from previous works, GTEA models the temporal dynamics of interaction
sequences in the continuous-time space and simultaneously takes advantage of
both rich node and edge/ interaction attributes in the graph. Concretely, we
integrate a sequence model with a time encoder to learn pairwise interactional
dynamics between two adjacent nodes.This helps capture complex temporal
interactional patterns of a node pair along the history, which generates edge
embeddings that can be fed into a GNN backbone. By aggregating features of
neighboring nodes and the corresponding edge embeddings, GTEA jointly learns
both topological and temporal dependencies of a TIG. In addition, a
sparsity-inducing self-attention scheme is incorporated for neighbor
aggregation, which highlights more important neighbors and suppresses trivial
noises for GTEA. By jointly optimizing the sequence model and the GNN backbone,
GTEA learns more comprehensive node representations capturing both temporal and
graph structural characteristics. Extensive experiments on five large-scale
real-world datasets demonstrate the superiority of GTEA over other inductive
models.
