论文标题
趋势：图表表示的时间事件和节点动力学
TREND: TempoRal Event and Node Dynamics for Graph Representation Learning
论文作者
论文摘要
时间图表示学习引起了人们对现实世界中时间图的普遍性的极大关注。但是，大多数现有的作品都诉诸于暂时图的离散快照，或者不诱导处理新节点，或者不模拟令人兴奋的效果，这是事件影响另一事件发生的能力。在这项工作中，我们提出了趋势，这是一个由时间事件和节点动力学驱动的时间图表示学习的新型框架，并建立在基于霍克斯过程的图形神经网络（GNN）上。趋势带来了一些主要优势：（1）由于其GNN架构，它具有归纳性； （2）它通过采用霍克斯进程捕获了事件之间的激动人心的影响； （3）作为我们的主要新颖性，它通过整合事件和节点动力学，推动时间过程的更精确的建模来捕获事件的个体和集体特征。四个现实世界数据集的广泛实验证明了我们提出的模型的有效性。
Temporal graph representation learning has drawn significant attention for the prevalence of temporal graphs in the real world. However, most existing works resort to taking discrete snapshots of the temporal graph, or are not inductive to deal with new nodes, or do not model the exciting effects which is the ability of events to influence the occurrence of another event. In this work, We propose TREND, a novel framework for temporal graph representation learning, driven by TempoRal Event and Node Dynamics and built upon a Hawkes process-based graph neural network (GNN). TREND presents a few major advantages: (1) it is inductive due to its GNN architecture; (2) it captures the exciting effects between events by the adoption of the Hawkes process; (3) as our main novelty, it captures the individual and collective characteristics of events by integrating both event and node dynamics, driving a more precise modeling of the temporal process. Extensive experiments on four real-world datasets demonstrate the effectiveness of our proposed model.
