  Temporal graphs offer more accurate modeling of many real-world scenarios
than static graphs. However, neighbor aggregation, a critical building block of
graph networks, for temporal graphs, is currently straightforwardly extended
from that of static graphs. It can be computationally expensive when involving
all historical neighbors during such aggregation. In practice, typically only a
subset of the most recent neighbors are involved. However, such subsampling
leads to incomplete and biased neighbor information. To address this
limitation, we propose a novel framework for temporal neighbor aggregation that
uses the recurrent neural network with node-wise hidden states to integrate
information from all historical neighbors for each node to acquire the complete
neighbor information. We demonstrate the superior theoretical expressiveness of
the proposed framework as well as its state-of-the-art performance in
real-world applications. Notably, it achieves a significant +9.6% improvement
on averaged precision in a real-world Ecommerce dataset over existing methods
on 2-layer models.
