Research map / Graph learning / Graph self-supervised learning
Graph-Relational Domain Adaptation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use a graph discriminator to align domains according to domain adjacency | domain adaptation on DG-15 · DG-15 | accuracy (%): 72.44 -> 84.44 | Helped |
| Use a graph discriminator to align domains according to domain adjacency | domain adaptation on DG-60 · DG-60 | accuracy (%): 66.24 -> 96.76 | Helped |
| Use a graph discriminator to align domains according to domain adjacency | domain adaptation on TPT-48 · E (24)→W (24), all domains | MSE: 1.335 -> 1.297 | Helped |
| Use a graph discriminator to align domains according to domain adjacency | domain adaptation on TPT-48 · N (24)→S (24), all domains | MSE: 2.054 -> 1.165 | Helped |
| Use a graph discriminator to align domains according to domain adjacency | domain adaptation on CompCars · CompCars | accuracy (%): 50.2 -> 51.0 | Helped |
| Use GRDA instead of MDD with an Adagraph backbone | CompCars · CompCars with Adagraph backbone | accuracy (%): 56.63 ± 0.64 -> 56.56 ± 0.38 | No clear effect |
| Increase the number of GRDA source domains | GRDA · DG-15 | accuracy: 64.7 -> 74.1 -> 81.5 -> 84.4 -> 100.0 | Helped |
| Use DANN rather than GRDA | domain adaptation · DG-60 | accuracy (%): 61.98 -> 96.76 | Hurt |
Closest papers by meaning
- Homophily Enhanced Graph Domain Adaptation (ICML 2025 · similarity 0.91)
- On the Benefits of Attribute-Driven Graph Domain Adaptation (ICLR 2025 · similarity 0.90)
- Open-Set Graph Domain Adaptation via Separate Domain Alignment (AAAI 2024 · similarity 0.90)
- Revisiting, Benchmarking and Understanding Unsupervised Graph Domain Adaptation (NeurIPS 2024 · similarity 0.90)
- Rethinking Propagation for Unsupervised Graph Domain Adaptation (AAAI 2024 · similarity 0.89)
- Pairwise Alignment Improves Graph Domain Adaptation (ICML 2024 · similarity 0.89)
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