APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding
This work is significant for applications requiring millisecond-level inference on dynamic graphs, such as financial fraud detection, by enabling real-time deployment of graph algorithms.
The paper addresses the challenge of real-time inference for graph algorithms, which are typically slow due to k-hop neighbor queries. They propose APAN, an asynchronous continuous-time dynamic graph algorithm, that decouples model inference from graph computation, achieving competitive performance and an 8.7 times inference speed improvement.
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.