  Temporal graphs exhibit dynamic interactions between nodes over continuous
time, whose topologies evolve with time elapsing.
  The whole temporal neighborhood of nodes reveals the varying preferences of
nodes.
  However, previous works usually generate dynamic representation with limited
neighbors for simplicity, which results in both inferior performance and high
latency of online inference.
  Therefore, in this paper, we propose a novel method of temporal graph
convolution with the whole neighborhood, namely Temporal Aggregation and
Propagation Graph Neural Networks (TAP-GNN).
  Specifically, we firstly analyze the computational complexity of the dynamic
representation problem by unfolding the temporal graph in a message-passing
paradigm.
  The expensive complexity motivates us to design the AP (aggregation and
propagation) block, which significantly reduces the repeated computation of
historical neighbors.
  The final TAP-GNN supports online inference in the graph stream scenario,
which incorporates the temporal information into node embeddings with a
temporal activation function and a projection layer besides several AP blocks.
  Experimental results on various real-life temporal networks show that our
proposed TAP-GNN outperforms existing temporal graph methods by a large margin
in terms of both predictive performance and online inference latency.
  Our code is available at \url{https://github.com/doujiang-zheng/TAP-GNN}.
