# Filtration-Based Representation Learning for Temporal Graphs

arXiv: https://arxiv.org/abs/2502.10076 · HTML: https://mlsanity.com/paper/2502.10076/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 14 Feb 2025.

## Builds on (most cited first, 10 of 10)

- [Temporal Graph Networks for Deep Learning on Dynamic Graphs](https://mlsanity.com/paper/2006.10637/) (2020)
- [Motifs in Temporal Networks](https://mlsanity.com/paper/1612.09259/) (2016)
- [A Stable Multi-Scale Kernel for Topological Machine Learning](https://mlsanity.com/paper/1412.6821/) (2014)
- [Temporal Graph Kernels for Classifying Dissemination Processes](https://mlsanity.com/paper/1911.05496/) (2019)
- [Temporal Network Analysis Using Zigzag Persistence](https://mlsanity.com/paper/2205.11338/) (2022)
- [Measuring large-scale social networks with high resolution](https://mlsanity.com/paper/1401.7233/) (2014)
- [DyANE: Dynamics-aware node embedding for temporal networks](https://mlsanity.com/paper/1909.05976/) (2019)
- [An embedding-based distance for temporal graphs](https://mlsanity.com/paper/2401.12843/) (2024)
- [Network Classification in Temporal Networks Using Motifs](https://mlsanity.com/paper/1807.03733/) (2018)
- [Time-Variant Graph Classification](https://mlsanity.com/paper/1609.04350/) (2016)

## Abstract (arXiv)

In this work, we introduce a filtration on temporal graphs based on δ-temporal motifs (recurrent subgraphs), yielding a multi-scale representation of temporal structure. Our temporal filtration allows tools developed for filtered static graphs, including persistent homology and recent graph filtration kernels, to be applied directly to temporal graph analysis. We demonstrate the effectiveness of this approach on temporal graph classification tasks.