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Robust Heterogeneous Graph Neural Networks against Adversarial Attacks.

, , , , , and . AAAI, page 4363-4370. AAAI Press, (2022)

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Unveiling the Secrets without Data: Can Graph Neural Networks Be Exploited through Data-Free Model Extraction Attacks?, , , , , , and . USENIX Security Symposium, USENIX Association, (2024)Endowing Pre-trained Graph Models with Provable Fairness., , , , , and . WWW, page 1045-1056. ACM, (2024)Towards Graph Foundation Models: A Survey and Beyond., , , , , , , , , and 1 other author(s). CoRR, (2023)An Integrated Semantic-Syntactic SBLSTM Model for Aspect Specific Opinion Extraction., , , , and . WISA, volume 11242 of Lecture Notes in Computer Science, page 191-199. Springer, (2018)Provable Training for Graph Contrastive Learning., , , , and . NeurIPS, (2023)Adversarial Label-Flipping Attack and Defense for Graph Neural Networks., , , and . ICDM, page 791-800. IEEE, (2020)Minimum Topology Attacks for Graph Neural Networks., , , , , and . WWW, page 630-640. ACM, (2023)Robust Heterogeneous Graph Neural Networks against Adversarial Attacks., , , , , and . AAAI, page 4363-4370. AAAI Press, (2022)Minimum Topology Attacks for Graph Neural Networks., , , , , and . CoRR, (2024)GraphTranslator: Aligning Graph Model to Large Language Model for Open-ended Tasks., , , , , , , , and . CoRR, (2024)