  Limited by the time complexity of querying k-hop neighbors in a graph
database, most graph algorithms cannot be deployed online and execute
millisecond-level inference. This problem dramatically limits the potential of
applying graph algorithms in certain areas, such as financial fraud detection.
Therefore, we propose Asynchronous Propagation Attention Network, an
asynchronous continuous time dynamic graph algorithm for real-time temporal
graph embedding. Traditional graph models usually execute two serial
operations: first graph computation and then model inference. We decouple model
inference and graph computation step so that the heavy graph query operations
will not damage the speed of model inference. Extensive experiments demonstrate
that the proposed method can achieve competitive performance and 8.7 times
inference speed improvement in the meantime.
