APAN：用于实时的时态图嵌入的异步传播注意力网络
APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding
摘要：
受图数据库中查询k跳邻居的时间复杂度的限制，大多数图算法不能在线部署并执行毫秒级推理。这个问题极大地限制了图算法在某些领域的应用潜力，例如金融欺诈检测。因此，我们提出了异步传播注意力网络，一个异步的连续时间动态图算法的实时的【形】时间图嵌入。传统的图模型通常执行两个串行操作：首先进行图计算，然后进行模型推理。我们将模型推理和图计算步骤解耦，使得繁重的图查询操作不会影响模型推理的速度。大量实验表明，该方法可以实现竞争性的性能和8.7倍的推理速度的同时提高。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
