Filtration-Based Representation Learning for Temporal Graphs
Not on the map. Why?
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 33 references that are in the map. First posted on arXiv 14 Feb 2025.
Builds on
10 of 33 references.
References · 10
- 2020Temporal Graph Networks for Deep Learning on Dynamic Graphscited by 521
- 2016Motifs in Temporal Networkscited by 101
- 2014A Stable Multi-Scale Kernel for Topological Machine Learningcited by 74
- 2019Temporal Graph Kernels for Classifying Dissemination Processescited by 14
- 2022Temporal Network Analysis Using Zigzag Persistencecited by 9
- 2019DyANE: Dynamics-aware node embedding for temporal networkscited by 7
- 2014Measuring large-scale social networks with high resolutioncited by 6
- 2018Network Classification in Temporal Networks Using Motifscited by 3
- 2024An embedding-based distance for temporal graphscited by 3
- 2016Time-Variant Graph Classificationcited by 2
Built on by
Not cited yet.