Auditing Privacy Risks in LLM-Enhanced Graph Neural Networks

arXiv:2608.25727 · cs.LG, cs.CR · Submitted 2026-08-26 · Read on arXiv

cs.LG, cs.CR

Submitted: 2026-08-26

Updated: 2026-10-07

Terminology

• GNNs — 54× in this paper · explained in 2 episodes
• Embeddings — 16× in this paper · explained in 2 episodes
• Accuracy — 14× in this paper · explained in 1 episode
• Differential Privacy — 12× in this paper · explained in 4 episodes
• AUC — 11× in this paper · explained in 2 episodes
• Computer and Communications Security — 4× in this paper
• Large Language Models (LLMs) — 4× in this paper · explained in 6 episodes
• In International Conference on Machine Learning — 3× in this paper
• Representation Learning — 3× in this paper · explained in 2 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Differential Privacy (DP) — 2× in this paper · explained in 7 episodes
• Graph Neural Networks (GNNs) — 2× in this paper · explained in 7 episodes
• Knowledge Graph — 2× in this paper · explained in 7 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Unified Framework — 2× in this paper · explained in 5 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adjacency Matrix — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Causal Reasoning — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Homophily Guessing Attack — 1× in this paper
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Node Infiltration Attack — 1× in this paper
• Tenth International Conference on Learning Representations — 1× in this paper
• Thirty-Third International Joint Conference on Artificial Intelligence — 1× in this paper

Sources

• Linq-Embed-Mistral Technical Report
• SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning
• Node-Level Membership Inference Attacks Against Graph Neural Networks
• Are LLM-Enhanced Graph Neural Networks Robust against Poisoning Attacks?
• LLaMA: Open and Efficient Foundation Language Models
• Text Embeddings by Weakly-Supervised Contrastive Pre-training
• Toward General and Robust LLM-enhanced Text-attributed Graph Learning

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