Learning on evolving(dynamic) graphs has caught the attention of researchers as static methods exhibit limited performance in this setting. The existing methods for dynamic graphs learn spatial features by local neighborhood aggregation, which essentially only captures the low pass signals and local interactions. In this work, we go beyond current approaches to incorporate global features for effectively learning representations of a dynamically evolving graph. We propose to do so by capturing the spectrum of the dynamic graph. Since static methods to learn the graph spectrum would not consider the history of the evolution of the spectrum as the graph evolves with time, we propose a novel approach to learn the graph wavelets to capture this evolving spectra. Further, we propose a framework that integrates the dynamically captured spectra in the form of these learnable wavelets into spatial features for incorporating local and global interactions. Experiments on eight standard datasets show that our method significantly outperforms related methods on various tasks for dynamic graphs.
翻译:有关进化(动态)图形的学习引起了研究人员的注意,因为静态方法在这一环境中的表现有限。动态图形的现有方法通过地方邻里汇总学习空间特征,基本上只能捕捉低传球信号和地方互动。在这项工作中,我们超越了目前的方法,将全球特征纳入到一个动态进化图的有效学习中。我们提议通过捕捉动态图的频谱来这样做。由于学习图形频谱的静态方法不会随着图形的演进而考虑到频谱的演变历史,我们建议了一种新颖的方法来学习图形波子来捕捉这个不断演化的光谱。此外,我们提出了一个框架,以这些可学的波子的形式将动态捕捉的光谱纳入空间特征中,以便将这些可学的波子纳入到地方和全球互动中。对八个标准数据集的实验表明,我们的方法大大超越了动态图形各项任务的相关方法。