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.

翻译：现有的动态图分类方法要么将图核扩展到时间域，要么使用图神经网络（GNNs）。然而，当前基线方法存在可扩展性问题，无法处理变化的节点集合，或未考虑边权重信息。我们提出过滤曲面（filtration surfaces）这一可扩展且灵活的新方法，以缓解上述限制。通过实验验证了该模型的有效性，结果表明在依赖边权重信息的数据集上，过滤曲面优于先前最先进的基线方法。我们的方法要么完全无参数，要么最多仅含一个参数，同时实现了最低的总体标准差。