arXiv:2606.00808v1 Announce Type: new Abstract: Source-free graph domain adaptation (SF-GDA) aims to adapt source-trained graph models to unlabeled target graphs when source graphs are no longer accessible. A central obstacle is pseudo-label reliability: under feature and topological shifts, source-induced predictions may become confidently wrong, and indiscriminate self-training can amplify systematic errors through graph message passing. This paper studies SF-GDA from a selective pseudo-labeling perspective. Instead of assuming globally bounded pseudo-label noise over the entire target domai
The proliferation of increasingly complex graph-based AI models necessitates robust domain adaptation techniques, especially when source data access is limited or constrained by privacy and cost.
Improving the reliability of pseudo-labeling in source-free graph domain adaptation is crucial for deploying AI in sensitive real-world applications where data shifts are common, impacting fields from drug discovery to cybersecurity.
This research introduces a more selective and robust approach to pseudo-labeling, moving beyond indiscriminate self-training to reduce the amplification of errors in graph AI models.
- · AI researchers and developers
- · Industries using graph AI (e.g., pharma, finance)
- · Secure/private computing platforms
- · AI models prone to catastrophic forgetting
- · Indiscriminate self-training methods
- · Applications relying on static, non-adaptive graph AI systems
More accurate and reliable AI deployments in dynamic, data-constrained environments.
Accelerated adoption of graph neural networks in critical infrastructure and privacy-sensitive sectors due to enhanced trustworthiness.
Reduced computational overhead and data dependency for model adaptation, democratizing advanced AI deployment.
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Read at arXiv cs.LG