ResearchResearch paperLarge Language Models · Interpretability · Training & Scaling1 source · Oct 15, 2026

Lethe: Link Inference Attacks For Evaluation of Edge Unlearning Methods

To address this gap, we introduce Lethe, the first comprehensive benchmark for Edge Unlearning, featuring 15 methods and 8 datasets.

Graph Unlearning (GU) aims to remove the influence of specific training data from a Graph Neural Network without retraining anew.
While most methods address removal of nodes and features, Edge Unlearning (EU), aiming at forgetting edges, has recently attracted dedicated attention as a standalone problem due to the hardness of completely removing the impact of an edge in the model.
As such, we show two systematic limitations in existing evaluations: dedicated EU methods can be slower than retraining anew, and accuracy metrics are uninformative due to the negligible impact of edge removal.
Our benchmark emphasizes large graphs, introduces evaluation tasks of varying difficulty, and proposes empirical measures based on link inference attacks rather than proxy accuracy metrics.

[1]Lethe: Link Inference Attacks For Evaluation of Edge Unlearning Methods
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 01:41 PM
To address this gap, we introduce Lethe, the first comprehensive benchmark for Edge Unlearning, featuring 15 methods and 8 datasets.
Graph Unlearning (GU) aims to remove the influence of specific training data from a Graph Neural Network without retraining anew.

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