Filtration Surfaces for Dynamic Graph Classification

Franz Srambical; Bastian Rieck

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arXiv · 2309.03616

Filtration Surfaces for Dynamic Graph Classification

Franz Srambical·Bastian Rieck

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Abstract

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.

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Franz Srambical, Bastian Rieck. 2023-09-07. Filtration Surfaces for Dynamic Graph Classification. https://arxiv.org/abs/2309.03616

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