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MUGEN framework generates unlearnable graph examples for multiple tasks

By PulseAugur Editorial · [1 sources] ·

Researchers have developed MUGEN, a novel framework designed to generate unlearnable graph examples that protect against misuse across multiple learning tasks simultaneously. Unlike previous methods that focus on a single task, MUGEN creates a single feature-perturbed release that safeguards against node classification, graph classification, and link prediction. The framework employs a Task-Aligned Separability Objective (TASO) to enhance unlearnability and a Type-Adaptive Perturbation (TAP) method to tailor the perturbation process to different data types, demonstrating effectiveness across various benchmarks and adversarial conditions. AI

IMPACT This research offers a new method for protecting graph data from unauthorized representation learning across various downstream tasks.

RANK_REASON The cluster contains an academic paper detailing a new framework for generating unlearnable graph examples. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

MUGEN framework generates unlearnable graph examples for multiple tasks

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The cluster contains an academic paper detailing a new framework for generating unlearnable graph examples. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ziyan Liu, Chengshuai Zhao, Huan Liu ·

    MUGEN: Generating Unlearnable Graph Examples for Multiple Learning Tasks

    arXiv:2609.00696v1 Announce Type: new Abstract: Graph data across diverse domains can expose valuable relational information to unauthorized representation learning, creating a pressing need for protection against such misuse. Unlearnable examples offer a data-level defense by pe…

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