February 22, 2019 preprint Open access Adversarial Attacks on Graph Neural Networks via Meta Learning arXiv (Cornell University) DOI: 10.48550/arxiv.1902.08412 Full text (OA) OpenAlex Authors 0 Author list not loaded yet. Cited by 4 stored of 373 Search Sort Most cited Newest Oldest Patent citations Title Any typearticle review book-chapter conference-paper preprint dissertation book dataset other Any fieldAgricultural and Biological Sciences Arts and Humanities Biochemistry, Genetics and Molecular Biology Business, Management and Accounting Chemical Engineering Chemistry Computer Science Decision Sciences Dentistry Earth and Planetary Sciences Economics, Econometrics and Finance Energy Engineering Environmental Science Health Professions Immunology and Microbiology Materials Science Mathematics Medicine Neuroscience Nursing Pharmacology, Toxicology and Pharmaceutics Physics and Astronomy Psychology Social Sciences Veterinary Open access How does Heterophily Impact the Robustness of Graph Neural Networks? 2022 ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) conference-paper Computer Science Adversarial Robustness in Machine Learning Open access 31 citations On the Relationship between Heterophily and Robustness of Graph Neural Networks. 2021 arXiv (Cornell University) preprint Computer Science Adversarial Robustness in Machine Learning Open access 2 citations Structack: Structure-based Adversarial Attacks on Graph Neural Networks 2021 ACM Conference on Hypertext and Social Media (HT) conference-paper Computer Science Adversarial Robustness in Machine Learning Open access 16 citations The Effects of Randomness on the Stability of Node Embeddings 2021 Communications in computer and information science conference-paper Computer Science Advanced Graph Neural Networks Open access 16 citations 4 results References 0