EdisonLeeeee/Graph-Adversarial-Learning
A curated collection of adversarial attack and defense on graph data.
This collection helps machine learning practitioners understand how to secure and evaluate the robustness of their Graph Neural Networks (GNNs). It gathers research papers on methods to intentionally mislead (attack) or fortify (defend) GNNs, organized by year and type. If you are developing or deploying GNNs for tasks like fraud detection, recommendation systems, or social network analysis, this resource helps you identify potential vulnerabilities and countermeasures.
580 stars. No commits in the last 6 months.
Use this if you are a machine learning engineer or data scientist working with Graph Neural Networks and need to research methods for adversarial attacks or defenses to ensure the reliability and security of your models.
Not ideal if you are looking for an out-of-the-box tool or library to directly implement graph adversarial attacks or defenses without diving into academic research.
Stars
580
Forks
77
Language
Python
License
GPL-3.0
Category
Last pushed
Nov 07, 2023
Commits (30d)
0
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