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Memorization in Graph Neural Networks
Adarsh Jamadandi, Jing Xu, Adam Dziedzic +1
Deep neural networks (DNNs) have been shown to memorize their training data, yet similar analyses for graph neural networks (GNNs) remain largely under-explored. We introduce NCMem…
Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs
Xun Wang, Jing Xu, Franziska Boenisch +3
Prompting has become a dominant paradigm for adapting large language models (LLMs). While discrete (textual) prompts are widely used for their interpretability, soft (parameter) pr…
DP-GPL: Differentially Private Graph Prompt Learning
Jing Xu, Franziska Boenisch, Iyiola Emmanuel Olatunji +1
Graph Neural Networks (GNNs) have shown remarkable performance in various applications. Recently, graph prompt learning has emerged as a powerful GNN training paradigm, inspired by…