Shypd.aiThe largest open index of AI toolsgraph-adversarial-learning-literature is an Open Source research tool that curates papers on adversarial attacks and defenses for graph-structured data. It provides a categorized list of academic papers for researchers.
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graph-adversarial-learning-literature is an open-source curated list of academic papers focusing on adversarial attacks and defenses within graph-structured data. This resource is designed for researchers and machine learning engineers interested in the robustness and security of graph neural networks. Papers are meticulously sorted by their upload dates in descending order, offering a chronological view of advancements in the field. The repository also includes quick links to attack and defense papers sorted by year, and provides a search functionality to locate papers by conference name, task name, model name, or method name. It serves as a complement to a comprehensive survey on the topic, with citation information provided for both Arxiv and TKDE versions of the survey.
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