Existing approaches for classifying dynamic graphs either lift graph kernels
to the temporal domain, or use graph neural networks (GNNs). However, current
baselines have scalability issues, cannot handle a changing node set, or do not
take edge weight information into account. We propose filtration surfaces, a
novel method that is scalable and flexible, to alleviate said restrictions. We
experimentally validate the efficacy of our model and show that filtration
surfaces outperform previous state-of-the-art baselines on datasets that rely
on edge weight information. Our method does so while being either completely
parameter-free or having at most one parameter, and yielding the lowest overall
standard deviation.

现有动态图分类方法要么将图内核扩展到时间域，要么使用图神经网络 (GNNs)。然而，当前基准存在可扩展性问题，不能处理变化的节点集，或者不考虑边权重信息。我们提出了滤波表面，一种具有可扩展性和灵活性的新方法，以缓解上述限制。我们在基于边权重信息的数据集上实验证实了模型的有效性，并展示滤波表面在性能上胜过先前的最优方法。我们的方法要么是完全无参数的，要么最多只有一个参数，并且产生最低的总体标准差。