AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Achieving strong performance with graph neural networks (GNNs) typically requires training and hyperparameter tuning for each dataset, incurring repeated costs and effort. Graph in-context learning (ICL) avoids this by using a single pretrained model to predict unknown node labels directly from labeled context nodes. E...
TaLK is proposed, an effective dataset distillation method for TAGs that couples an LM with a graph-aware neural tangent kernel that enables efficient dataset distillation, avoiding repeated joint training on the full dataset while reflecting both textual and structural information for effective TAG learning.
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