arXiv Machine Learning
Data-Efficient Agentic Graph Domain Adaptation via Reliability-Aware Prototype Learning
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Graph Domain Adaptation Does Not End with Representation Learning
arXiv:2609.25692v1 Announce Type: new Abstract: Graph domain adaptation (GDA) transfers knowledge from a labeled source graph to an unlabeled target graph under shifts in both node attributes and gra...
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Cross-Resolution Semantic Learning for Graph Domain Adaptation
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The paper introduces Adaptive Dual-Teacher Distillation with Subnetwork Rectification (DDSR), a method for black-box domain adaptation that fuses predictions from a black-box source model and a vision‑language model to generate reliable pseudo‑labels for the target domain. DDSR uses adaptive prediction fusion, subnetwork regularization to prevent overfitting, iterative refinement of pseudo‑labels and prompts, and class‑wise prototypes for self‑training. Experiments on several benchmark datasets show that DDSR outperforms existing state‑of‑the‑art approaches, even those that have access to source data or source model parameters.
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Selectivity Matters: Source Node Influence Pruning for Unsupervised Graph Domain Adaptation
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arXiv AI
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Handling Feature Heterogeneity with Learnable Graph Patches
arXiv:2606. 17667v1 Announce Type: cross Abstract: In recent years, the rapid development of foundation models and graph pre-training technologies has spurred increasing interest in constructing a universal pre-trained graph model or Graph Foundation Model (GFM).