Research map / Graph learning / Graph self-supervised learning
Deep Learning with Topological Signatures
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the learned topological-signature input layer for graph classification | reddit-5k and reddit-12k · reddit-5k and reddit-12k | classification accuracy | Helped |
| Include essential topological features (ablation) | the proposed graph-classification method · reddit-5k and reddit-12k | classification accuracy | Helped |
| Use the learned topological-signature input layer for 2D shape classification | Animal and MPEG-7 · Animal and MPEG-7 | classification accuracy | Mixed |
| Use the proposed learned representation instead of persistence-diagram vectorization with a linear SVM | the proposed method · Animal, MPEG-7, reddit-5k, and reddit-12k | classification accuracy | Helped |
Closest papers by meaning
- Topological Blindspots: Understanding and Extending Topological Deep Learning Through the Lens of Expressivity (ICLR 2025 · similarity 0.89)
- Point-Level Topological Representation Learning on Point Clouds (ICML 2025 · similarity 0.88)
- Cover learning for large-scale topology representation (ICML 2025 · similarity 0.87)
- Towards Scalable Topological Regularizers (ICLR 2025 · similarity 0.87)
- Copresheaf Topological Neural Networks: A Generalized Deep Learning Framework (NeurIPS 2025 · similarity 0.86)
- MANTRA: The Manifold Triangulations Assemblage (ICLR 2025 · similarity 0.86)
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