# Filtration Surfaces for Dynamic Graph Classification

arXiv: https://arxiv.org/abs/2309.03616 · HTML: https://mlsanity.com/paper/2309.03616/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 36 references that are in the map.
- First posted on arXiv 7 Sep 2023.

## Builds on (most cited first, 18 of 18)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [TUDataset: A collection of benchmark datasets for learning with graphs](https://mlsanity.com/paper/2007.08663/) (2020)
- [Graph Transformer Networks](https://mlsanity.com/paper/1911.06455/) (2019)
- [PersLay: A Neural Network Layer for Persistence Diagrams and New Graph Topological Signatures](https://mlsanity.com/paper/1904.09378/) (2019)
- [Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey](https://mlsanity.com/paper/2005.07496/) (2020)
- [Wasserstein Weisfeiler-Lehman Graph Kernels](https://mlsanity.com/paper/1906.01277/) (2019)
- [Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention](https://mlsanity.com/paper/2105.13495/) (2021)
- [Dynamic Graph Convolutional Networks](https://mlsanity.com/paper/1704.06199/) (2017)
- [Graph Filtration Learning](https://mlsanity.com/paper/1905.10996/) (2019)
- [Multi-scale Dynamic Graph Convolutional Network for Hyperspectral Image Classification](https://mlsanity.com/paper/1905.06133/) (2019)
- [Mondrian Forests: Efficient Online Random Forests](https://mlsanity.com/paper/1406.2673/) (2014)
- [Temporal Graph Kernels for Classifying Dissemination Processes](https://mlsanity.com/paper/1911.05496/) (2019)
- [Towards Open Temporal Graph Neural Networks](https://mlsanity.com/paper/2303.15015/) (2023)
- [Dynamic Graph Echo State Networks](https://mlsanity.com/paper/2110.08565/) (2021)
- [Time-Variant Graph Classification](https://mlsanity.com/paper/1609.04350/) (2016)
- [Dynamical Graph Echo State Networks with Snapshot Merging for Dissemination Process Classification](https://mlsanity.com/paper/2307.01237/) (2023)

## Built on by (most cited first, 1 of 1)

- [CliquePH: Higher-Order Information for Graph Neural Networks through Persistent Homology on Clique Graphs](https://mlsanity.com/paper/2409.08217/) (2024)

## Abstract (arXiv)

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.