GP2F: Cross-Domain Graph Prompting with Adaptive Fusion of Pre-trained Graph Neural Networks
arXiv:2602.11629v2 Announce Type: replace Abstract: Graph Prompt Learning (GPL) has recently emerged as a promising paradigm for downstream adaptation of pre-trained graph models, mitigating the misalignment between pre-training objectives and downstream tasks. Recently, the focus of GPL has shifted from in-domain to cross-domain scenarios, which is closer to the real world applications, where the pre-training source and downstream target often differ substantially in data distribution. However, why GPLs remain effective under such domain shifts is still unexplored. Empirically, we observe tha
The proliferation of pre-trained AI models across diverse domains necessitates more efficient adaptation methods, making techniques like Graph Prompt Learning critical for real-world application.
This research advances the ability of AI models to generalize across different data distributions, significantly broadening the applicability and effectiveness of graph neural networks in complex, real-world scenarios.
The improved cross-domain adaptability of graph models allows for more robust and versatile AI systems, reducing the need for extensive retraining and domain-specific model development.
- · AI developers
- · Data scientists
- · Industries with diverse data sources
- · Developers of highly specialized, in-domain models
Graph-based AI applications become more resilient and generalizable across varying datasets.
This could accelerate the deployment of advanced AI in fields like drug discovery, material science, and social network analysis where data distributions often vary.
It might democratize access to sophisticated graph AI capabilities by lowering the bar for model adaptation and reducing computational overhead.
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Read at arXiv cs.LG