  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 among similarly scalable methods.
