arXiv:2605.25771v1 Announce Type: new Abstract: Multi-domain graph pre-training is a crucial step in constructing foundational graph models with cross-domain generalization capabilities. However, existing methods predominantly rely on jointly training all source domain graphs, resulting in high computational costs. Furthermore, it remains unclear whether all source domain graph data contribute equally to effective transfer. This paper empirically reveals significant data redundancy in multi-domain graph pre-training. Based on this finding, we propose the Multi-domain Graph Pre-training Framewo
The proliferation of various graph models and the drive for more efficient AI pre-training methods are pushing research into multi-domain approaches at this moment.
This research addresses the high computational cost and data redundancy in multi-domain graph pre-training, offering a path to more efficient and scalable foundational AI models.
The focus shifts towards optimizing multi-domain graph pre-training, potentially leading to more resource-efficient development of generalized AI, moving away from brute-force joint training.
- · AI model developers
- · Cloud computing providers (reduced cost)
- · Graph AI startups
- · Academic AI research
- · Less efficient AI training methods
- · Organizations with limited compute resources applying older methods
More efficient and cost-effective development of foundational graph AI models becomes possible.
This could accelerate the deployment of AI in diverse applications by lowering the barrier to entry and improving model generalization.
Improved efficiency in AI training might contribute to the broader 'compute supply chain' challenge by optimizing existing resources and reducing future demand growth rates.
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Read at arXiv cs.LG