GaRA generates task-specific LoRA weight updates conditioned on graph structures to enable better whole-graph encoding in LLMs for zero-shot graph learning.
arXiv preprint arXiv:2007.12374 , year=
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Random Add-Drop Edge (RADE) jointly drops and adds edges stochastically during GNN training, with provable train-inference alignment and an adaptive rate balancer, to regularize against overfitting and mitigate over-squashing.
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Enhancing LLMs for Graph Tasks via Graph-aware LoRA Generation
GaRA generates task-specific LoRA weight updates conditioned on graph structures to enable better whole-graph encoding in LLMs for zero-shot graph learning.
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RADE: Random Add-Drop Edge as a Regularizer
Random Add-Drop Edge (RADE) jointly drops and adds edges stochastically during GNN training, with provable train-inference alignment and an adaptive rate balancer, to regularize against overfitting and mitigate over-squashing.