arXiv Machine Learning
2d ago

MUGEN: Generating Unlearnable Graph Examples for Multiple Learning Tasks

MUGEN is a framework that generates unlearnable graph examples capable of protecting multiple downstream tasks—node classification, graph classification, and link prediction—simultaneously. It achieves this by perturbing a single clean dataset with a shared GNN encoder and task‑specific heads, guided by a Task‑Aligned Separability Objective (TASO) and a Type‑Adaptive Perturbation (TAP) that handles both discrete and continuous node attributes. Experiments on five benchmarks, four GNN backbones, and three learning paradigms show that MUGEN’s perturbations transfer across models and remain effective even under adversarial training and data augmentation.

By Ziyan Liu, Chengshuai Zhao, Huan Liu
arXiv Machine Learning
Jun 8

ADAGE: Active Defenses Against GNN Extraction

arXiv:2503. 00065v4 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) achieve high performance in various real-world applications, such as drug discovery, traffic states prediction, and recommendation systems.

By Jing Xu, Franziska Boenisch, Adam Dziedzic