APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding

Autor:	Wang, Xuhong, Lyu, Ding, Li, Mengjian, Xia, Yang, Yang, Qi, Wang, Xinwen, Wang, Xinguang, Cui, Ping, Yang, Yupu, Sun, Bowen, Guo, Zhenyu
Rok vydání:	2020
Předmět:	Computer Science - Artificial Intelligence Computer Science - Databases Computer Science - Social and Information Networks
Druh dokumentu:	Working Paper
DOI:	10.1145/3448016.3457564
Popis:	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. Comment: In Proceedings of the 2021 International Conference on Management of Data (SIGMOD/PODS '21)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2011.11545 Zobrazit plný text záznamu View this record from Arxiv