Research map / Graph learning / Graph self-supervised learning
Rethinking Propagation for Unsupervised Graph Domain Adaptation
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's full text. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Revisits GNN propagation for unsupervised graph domain adaptation and proposes A2GNN, an asymmetric architecture (MLP on source, multi-layer propagation on target) with a Lipschitz-based generalization bound.
Problem
Unsupervised graph domain adaptation (UGDA) for node classification under distribution shift between source and target graphs; prior work focuses on alignment in GNN representation space and overlooks GNNs' own generalization capability.
Approach
Decouples GNNs into propagation (P) and transformation (T) operations and finds propagation on the target graph drives adaptation; uses an asymmetric design with no propagation on the source (single transformation) and k propagation layers on the target, with shared weights, plus MMD or adversarial alignment, and a Lipschitz-based bound showing a tighter target risk.
Key results
- A2GNN_mmd reaches 75.69 Macro-F1 on D→A, up 3.05% over the best baseline (ACDNE 72.64).
- On Twitch EN→DE, A2GNN_adv reaches 62.03 Macro-F1 vs 57.92 for the best baseline ACDNE (+4.11%).
- Averaged over citation tasks, A2GNN_mmd improves Macro-F1 by about 1.80% over the best baselines.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add MMD alignment (ablation) | A2GNN_mmd · D→A | Macro-F1: 58.50 -> 67.09 | Helped |
| add MMD alignment (ablation) | A2GNN_mmd · D→C | Macro-F1: 66.78 -> 72.96 | Helped |
| add k propagation layers (ablation) | A2GNN_mmd with MMD alignment · D→A | Macro-F1: 67.09 -> 72.41 | Helped |
| add k propagation layers (ablation) | A2GNN_mmd with MMD alignment · D→C | Macro-F1: 72.96 -> 78.00 | Helped |
| add asymmetric architecture (ablation) | A2GNN_mmd with MMD alignment and k propagation layers · D→A | Macro-F1: 72.41 -> 75.69 | Helped |
| add asymmetric architecture (ablation) | A2GNN_mmd with MMD alignment and k propagation layers · D→C | Macro-F1: 78.00 -> 79.74 | Helped |
| replace symmetric normalized propagation with no-loop propagation (ablation) | A2GNN_mmd · D→A | Macro-F1: 75.69 -> 75.96 | No clear effect |
| replace symmetric normalized propagation with no-loop propagation (ablation) | A2GNN_mmd · D→C | Macro-F1: 79.74 -> 78.69 | Hurt |
| replace symmetric normalized propagation with random-walk propagation (ablation) | A2GNN_mmd · D→C | Macro-F1: 79.74 -> 78.10 | Hurt |
| replace symmetric normalized propagation with random-walk propagation (ablation) | A2GNN_mmd · D→A | Macro-F1: 75.69 -> 75.77 | No clear effect |
| replace symmetric normalized propagation with heat-kernel diffusion (ablation) | A2GNN_mmd · D→A | Macro-F1: 75.69 -> 74.63 | Hurt |
| replace symmetric normalized propagation with heat-kernel diffusion (ablation) | A2GNN_mmd · D→C | Macro-F1: 79.74 -> 77.48 | Hurt |
Surprising findings
- Propagation on the source graph is unnecessary and performing propagation only on the target graph gives better performance than on both graphs (1.85% Macro-F1 gain).
- Some domain adaptation methods underperform source-only GNNs on social networks, attributed to negative transfer on heterophilic graphs.
Limitations
- Theoretical bound relies on assumptions such as bounded features and weights, and no theoretical evidence that more layers always tighten the bound.
- Experiments limited to node classification on small citation and Twitch social networks.
- Results depend on choice of alignment loss (MMD or adversarial), with MMD generally stronger.
Experimental setup
Datasets: ACMv9 Citationv1 DBLPv7 Twitch Germany Twitch England
Benchmarks: ACMv9-Citationv1-DBLPv7 UDA tasks (D→A, C→A, A→D, C→D, A→C, D→C) Twitch DE↔EN
Metrics: Macro-F1 Micro-F1
Baselines: DeepWalk node2vec ANRL GAT GraphSAGE GIN GCN CDNE UDAGCN ACDNE ASN AdaGCN
Builds on: GCN MMD domain adaptation domain-adversarial training (gradient reversal) SpecReg generalization bound
Closest papers by meaning
- Revisiting, Benchmarking and Understanding Unsupervised Graph Domain Adaptation (NeurIPS 2024 · similarity 0.95)
- Domain Adaptive Unfolded Graph Neural Networks (AAAI 2025 · similarity 0.93)
- Smoothness Really Matters: A Simple Yet Effective Approach for Unsupervised Graph Domain Adaptation (AAAI 2025 · similarity 0.93)
- On the Benefits of Attribute-Driven Graph Domain Adaptation (ICLR 2025 · similarity 0.90)
- Towards Unsupervised Open-Set Graph Domain Adaptation via Dual Reprogramming (NeurIPS 2025 · similarity 0.90)
- Open-Set Graph Domain Adaptation via Separate Domain Alignment (AAAI 2024 · similarity 0.90)
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.