Research map / Graph learning / Graph self-supervised learning
Learning metrics for persistence-based summaries and applications for graph classification
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Learn the persistence-image kernel weight function using WKPI-kM | WKPI graph classification · NCI1 | accuracy: 85.6±0.3 -> 87.5±0.5 | Helped |
| Learn the persistence-image kernel weight function using WKPI-kC | WKPI graph classification · PTC | accuracy: 66.6±6.9 -> 68.1±2.4 | Helped |
| Use WKPI-kC metric learning | WKPI graph classification · MUTAG | accuracy: 90.3±1.1 -> 88.3±2.6 | Hurt |
| Use WKPI-kC metric learning | WKPI graph classification · IMDB-MULTI | accuracy: 52.3 ±2.8 -> 49.5±0.4 | Hurt |
| Use WKPI-kM metric learning | WKPI graph classification · REDDIT-12K | accuracy: 48.7±0.2 -> 47.4±0.6 | Hurt |
| Initialize Gaussian weight centers with k-means instead of random sampling (ablation) | WKPI weight-function learning · graph classification datasets | accuracy | Helped |
| Initialize Gaussian weight centers with k-centers instead of random sampling (ablation) | WKPI weight-function learning · graph classification datasets | accuracy | Helped |
| Replace WKPI with altWKPI kernel (ablation) | persistence-image metric learning · MUTAG | accuracy: 85.5 -> 88.5 | Helped |
| Replace WKPI with altWKPI kernel (ablation) | persistence-image metric learning · NCI1 | accuracy: 87.2 -> 77.4 | Hurt |
| Learn weights in PWGK instead of using WKPI (ablation) | persistence-based graph classification · NCI1 | accuracy: 87.2 -> 76.5 | Hurt |
| Learn weights in PWGK instead of using WKPI (ablation) | persistence-based graph classification · REDDIT-5K | accuracy: 59.3 -> 53.5 | Hurt |
| Learn weights in PWGK instead of using WKPI (ablation) | persistence-based graph classification · PTC | accuracy: 64.3 -> 62.5 | Mixed |
Closest papers by meaning
- A Persistent Weisfeiler-Lehman Procedure for Graph Classification (ICML 2019 · similarity 0.89)
- The Persistent Laplacian for Data Science: Evaluating Higher-Order Persistent Spectral Representations of Data (ICML 2023 · similarity 0.89)
- Graphcode: Learning from multiparameter persistent homology using graph neural networks (NeurIPS 2024 · similarity 0.87)
- Graph Persistence goes Spectral (NeurIPS 2025 · similarity 0.86)
- TopoGCL: Topological Graph Contrastive Learning (AAAI 2024 · similarity 0.86)
- Differentiability and Optimization of Multiparameter Persistent Homology (ICML 2024 · similarity 0.86)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.