论文标题
时间感知的随机步行扩散以改善动态图学习
Time-aware Random Walk Diffusion to Improve Dynamic Graph Learning
论文作者
论文摘要
我们如何增加动态图来改善动态图神经网络的性能？图表增强已被广泛用于提高基于GNN的模型的学习性能。但是，大多数现有方法仅通过转换图形来增强输入静态图内的空间结构，并且不考虑由时间位置等时间引起的动态，即最近的边缘比早期的边缘更具影响力，这对于动态图增强仍然具有挑战性。在这项工作中，我们提出了TIARA（时间感知随机步行扩散），这是一种基于新型扩散的方法，用于增强作为图形快照的离散时间序列表示动态图。为此，我们首先设计一个时间感知的随机步行接近度，以便冲浪者可以沿时间维度和边缘行走，从而在空间和时间上定位得分。然后，我们根据时间感知的随机步行得出扩散矩阵，并表明它们成为增强的邻接矩阵，表明空间和时间位置都得到了增强。在整个广泛的实验中，我们证明了Tiara有效地增强了给定的动态图，并导致各种图形数据集和任务的动态GNN模型的显着改善。
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.
