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.
@article{arxiv.2502.10076,
title = {Filtration-Based Representation Learning for Temporal Graphs},
author = {Samrik Chowdhury and Siddharth Pritam and Rohit Roy and Madhav Cherupilil Sajeev},
journal= {arXiv preprint arXiv:2502.10076},
year = {2025}
}