How can we augment a dynamic graph for improving the performance of dynamic graph neural networks? Graph augmentation has been widely utilized to boost the learning performance of GNN-based models. However, most existing approaches only enhance spatial structure within an input static graph by transforming the graph, and do not consider dynamics caused by time such as temporal locality, i.e., recent edges are more influential than earlier ones, which remains challenging for dynamic graph augmentation. In this work, we propose TiaRa (Time-aware Random Walk Diffusion), a novel diffusion-based method for augmenting a dynamic graph represented as a discrete-time sequence of graph snapshots. For this purpose, we first design a time-aware random walk proximity so that a surfer can walk along the time dimension as well as edges, resulting in spatially and temporally localized scores. We then derive our diffusion matrices based on the time-aware random walk, and show they become enhanced adjacency matrices that both spatial and temporal localities are augmented. Throughout extensive experiments, we demonstrate that TiaRa effectively augments a given dynamic graph, and leads to significant improvements in dynamic GNN models for various graph datasets and tasks.
翻译:我们怎样才能增加动态图形神经网络的性能? 图形扩增已被广泛用来提高GNN模型的学习性能。 然而,大多数现有方法只是通过改变图形来提高输入静态图的空间结构,而没有考虑到时间地点等时间造成的动态,即最近的边缘比早期的更具有影响力,对动态图形扩增仍然具有挑战性。在这项工作中,我们提议了Tiara(时间觉随机散射),这是一种基于扩散的新方法,用来增加以离散时间顺序表示的动态图,作为图形抓图的分时间序列。为此,我们首先设计一个有时间意识的随机行走近点,以便冲浪者能够沿时间尺寸和边缘行走,从而产生空间和时间局部的分数。我们然后根据时间觉随机行走来得出我们的传播矩阵,并显示它们会增强空间和时间地点的相邻矩阵。我们通过广泛的实验,证明Tiara有效地增加了一个给定动态图表,并导致各种图形和任务动态GNNM模型的显著改进。