safe-graph/graph-adversarial-learning-literature
A curated list of adversarial attacks and defenses papers on graph-structured data.
This is a comprehensive collection of research papers focused on making machine learning models that operate on graph data more robust against attacks, and also on developing methods to attack them. It provides a list of papers, categorized by whether they describe an attack or a defense, helping you understand vulnerabilities and protective measures for graph-based AI. Researchers and practitioners working with graph neural networks in fields like social network analysis, fraud detection, or drug discovery would find this resource useful.
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Use this if you are a researcher or AI practitioner investigating the security and robustness of machine learning models built on graph-structured data, and need to find the latest research on adversarial attacks and defenses.
Not ideal if you are looking for ready-to-use software libraries or practical guides for implementing graph machine learning solutions, as this resource focuses purely on academic literature.
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Dec 15, 2023
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