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.

翻译：由于在图表数据库中查询 k- hop 邻居的时间复杂性有限,大多数图表算法无法在线部署并进行毫秒级的推算。 这个问题极大地限制了在某些领域应用图形算法的可能性, 如金融欺诈检测。 因此, 我们提议使用Asyncronous Propagation Convention Network, 这是实时时间图嵌入的非同步连续时间动态图形算法。 传统的图表模型通常执行两个序列操作: 第一次图形计算, 然后是模型推论。 我们通过模型推论和图形计算步骤, 以便重图形查询操作不会破坏模型推算的速度。 广泛的实验表明, 拟议的方法可以实现竞争性的性能, 同时还可以实现8. 7 次推论速度的改进。