Attacking Graph Foundation Models Through Their Shared Representation | Paper Citation Record

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This paper cites IEEE Transactions on Pattern Analysis and Machine Intelligence , year =.

Attacking Graph Foundation Models Through Their Shared Representation IEEE Transactions on Pattern Analysis and Machine Intelligence , year =

Reference 1

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This paper cites 2025 , eprint =.

Attacking Graph Foundation Models Through Their Shared Representation 2025 , eprint =

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This paper cites Position: Graph Foundation Models are Already Here.

Attacking Graph Foundation Models Through Their Shared Representation Position: Graph Foundation Models are Already Here

Reference 3

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This paper cites A Survey on Self-Supervised Graph Foundation Models: Knowledge-Based Perspective.

Attacking Graph Foundation Models Through Their Shared Representation A Survey on Self-Supervised Graph Foundation Models: Knowledge-Based Perspective

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This paper cites International Conference on Learning Representations (ICLR) , year=.

Attacking Graph Foundation Models Through Their Shared Representation International Conference on Learning Representations (ICLR) , year=

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This paper cites Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) , year=.

Attacking Graph Foundation Models Through Their Shared Representation Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) , year=

Reference 6

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Attacking Graph Foundation Models Through Their Shared Representation International Conference on Machine Learning (ICML) , year=

Reference 7

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This paper cites Advances in Neural Information Processing Systems (NeurIPS) , year=.

Attacking Graph Foundation Models Through Their Shared Representation Advances in Neural Information Processing Systems (NeurIPS) , year=

Reference 8

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Observation f19f3bdf-c2ef-4287-b0b2-c8dd6114dfbd · outbound

This paper cites PRODIGY: Enabling In-context Learning Over Graphs.

Attacking Graph Foundation Models Through Their Shared Representation PRODIGY: Enabling In-context Learning Over Graphs

Reference 9

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Observation f490be06-2b68-408c-8d61-ddfed1d756f0 · outbound

This paper cites UniGraph: Learning a Unified Cross-Domain Foundation Model for Text-Attributed Graphs.

Attacking Graph Foundation Models Through Their Shared Representation UniGraph: Learning a Unified Cross-Domain Foundation Model for Text-Attributed Graphs

Reference 10

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Attacking Graph Foundation Models Through Their Shared Representation 2025 , eprint=

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This paper cites LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings.

Attacking Graph Foundation Models Through Their Shared Representation LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings

Reference 12

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Attacking Graph Foundation Models Through Their Shared Representation Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) , year=

Reference 13

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Attacking Graph Foundation Models Through Their Shared Representation IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) , year=

Reference 14

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Attacking Graph Foundation Models Through Their Shared Representation 2025 , eprint=

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Attacking Graph Foundation Models Through Their Shared Representation Advances in Neural Information Processing Systems (NeurIPS) , year=

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Attacking Graph Foundation Models Through Their Shared Representation 2023 , eprint=

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Attacking Graph Foundation Models Through Their Shared Representation International Conference on Learning Representations (ICLR) , year=

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Attacking Graph Foundation Models Through Their Shared Representation International Conference on Learning Representations (ICLR) , year=

Reference 19

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This paper cites Findings of the Association for Computational Linguistics: EACL 2024 , year=.

Attacking Graph Foundation Models Through Their Shared Representation Findings of the Association for Computational Linguistics: EACL 2024 , year=

Reference 20

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This paper cites HiGPT: Heterogeneous Graph Language Model.

Attacking Graph Foundation Models Through Their Shared Representation HiGPT: Heterogeneous Graph Language Model

Reference 21

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This paper cites GITA: Graph to Visual and Textual Integration for Vision-Language Graph Reasoning.

Attacking Graph Foundation Models Through Their Shared Representation GITA: Graph to Visual and Textual Integration for Vision-Language Graph Reasoning

Reference 22

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This paper cites Efficient Tuning and Inference for Large Language Models on Textual Graphs.

Attacking Graph Foundation Models Through Their Shared Representation Efficient Tuning and Inference for Large Language Models on Textual Graphs

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Attacking Graph Foundation Models Through Their Shared Representation Can GNN be Good Adapter for LLMs?

Reference 24

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Attacking Graph Foundation Models Through Their Shared Representation International Conference on Learning Representations (ICLR) , year=

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Attacking Graph Foundation Models Through Their Shared Representation Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) , year=

Reference 26

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Attacking Graph Foundation Models Through Their Shared Representation Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) , year=

Reference 27

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Attacking Graph Foundation Models Through Their Shared Representation International Conference on Learning Representations (ICLR) , year=

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This paper cites Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective.

Attacking Graph Foundation Models Through Their Shared Representation Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective

Reference 29

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Attacking Graph Foundation Models Through Their Shared Representation Adversarial Attack on Graph Structured Data

Reference 30

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Attacking Graph Foundation Models Through Their Shared Representation Proceedings of The Web Conference 2020 , pages =

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This paper cites Scalable Attack on Graph Data by Injecting Vicious Nodes.

Attacking Graph Foundation Models Through Their Shared Representation Scalable Attack on Graph Data by Injecting Vicious Nodes

Reference 32

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Attacking Graph Foundation Models Through Their Shared Representation TDGIA:Effective Injection Attacks on Graph Neural Networks

Reference 33

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Attacking Graph Foundation Models Through Their Shared Representation Single Node Injection Attack against Graph Neural Networks

Reference 34

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Attacking Graph Foundation Models Through Their Shared Representation Backdoor Attacks to Graph Neural Networks

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Attacking Graph Foundation Models Through Their Shared Representation Proceedings of the 36th International Conference on Machine Learning (ICML) , series=

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Attacking Graph Foundation Models Through Their Shared Representation ACM SIGKDD Explorations Newsletter , volume=

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Attacking Graph Foundation Models Through Their Shared Representation IEEE Transactions on Knowledge and Data Engineering , year=

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Attacking Graph Foundation Models Through Their Shared Representation Unveiling the Vulnerability of Graph-

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Attacking Graph Foundation Models Through Their Shared Representation Proceedings of the ACM Web Conference (WWW) , year =

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Attacking Graph Foundation Models Through Their Shared Representation IEEE Symposium on Security and Privacy (S&P) , year =

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Attacking Graph Foundation Models Through Their Shared Representation 2025 , eprint =

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Attacking Graph Foundation Models Through Their Shared Representation 2025 , eprint =

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Attacking Graph Foundation Models Through Their Shared Representation Attacks on Node Attributes in Graph Neural Networks

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Attacking Graph Foundation Models Through Their Shared Representation Ignore Previous Prompt: Attack Techniques For Language Models

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Attacking Graph Foundation Models Through Their Shared Representation Not What You've Signed Up For: Compromising Real-World

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Attacking Graph Foundation Models Through Their Shared Representation AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models

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Attacking Graph Foundation Models Through Their Shared Representation Findings of the Association for Computational Linguistics: ACL 2024 , year =

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Attacking Graph Foundation Models Through Their Shared Representation Proceedings of the 2025 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL) , year =

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Attacking Graph Foundation Models Through Their Shared Representation Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL) , year =

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Attacking Graph Foundation Models Through Their Shared Representation Findings of the Association for Computational Linguistics: ACL 2024 , year =

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Attacking Graph Foundation Models Through Their Shared Representation Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) , year =

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Attacking Graph Foundation Models Through Their Shared Representation Are You Using Reliable Graph Prompts? Trojan Prompt Attacks on Graph Neural Networks

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Attacking Graph Foundation Models Through Their Shared Representation Cross-Paradigm Graph Backdoor Attacks with Promptable Subgraph Triggers

Reference 63

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Attacking Graph Foundation Models Through Their Shared Representation HeTa: Relation-wise Heterogeneous Graph Foundation Attack Model

Reference 64

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Reference 65

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Reference 66

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Observation cc5f353e-dd38-4593-ac64-6450d12f9bad · outbound

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Attacking Graph Foundation Models Through Their Shared Representation GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks

Reference 67

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Attacking Graph Foundation Models Through Their Shared Representation All in One: Multi-task Prompting for Graph Neural Networks

Reference 68

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Reference 69

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Attacking Graph Foundation Models Through Their Shared Representation Proceedings of the AAAI Conference on Artificial Intelligence , author=

Reference 70

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Reference 71

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Reference 72

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Attacking Graph Foundation Models Through Their Shared Representation OpenGraph: Towards Open Graph Foundation Models

Reference 73

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Attacking Graph Foundation Models Through Their Shared Representation A ny G raph: Graph Foundation Model in the Wild

Reference 74

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Reference 75

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Reference 76

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Attacking Graph Foundation Models Through Their Shared Representation 2024 , eprint =

Reference 77

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Reference 78

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Reference 79

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Observation a4024a2c-2ff0-48f3-9eb0-a1b97f5983c8 · outbound

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Attacking Graph Foundation Models Through Their Shared Representation Advances in Neural Information Processing Systems (NeurIPS) , year =

Reference 80

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Attacking Graph Foundation Models Through Their Shared Representation Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) , year =

Reference 81

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Attacking Graph Foundation Models Through Their Shared Representation 2025 , eprint =

Reference 82

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Attacking Graph Foundation Models Through Their Shared Representation 2025 , eprint =

Reference 83

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Reference 84

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Attacking Graph Foundation Models Through Their Shared Representation Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights

Reference 85

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Attacking Graph Foundation Models Through Their Shared Representation Proceedings of the 42nd International Conference on Machine Learning (ICML) , year =

Reference 86

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Observation 2b2ba696-48d2-4eae-9bec-703d973e92f0 · outbound

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Attacking Graph Foundation Models Through Their Shared Representation The Platonic Representation Hypothesis

Reference 87

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Attacking Graph Foundation Models Through Their Shared Representation 2026 , eprint =

Reference 88

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Attacking Graph Foundation Models Through Their Shared Representation 2023 , eprint =

Reference 89

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Attacking Graph Foundation Models Through Their Shared Representation Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection

Reference 90

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Attacking Graph Foundation Models Through Their Shared Representation Similarity of Neural Network Representations Revisited

Reference 91

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Attacking Graph Foundation Models Through Their Shared Representation Locating and Editing Factual Associations in

Reference 92

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Attacking Graph Foundation Models Through Their Shared Representation On Adaptive Attacks to Adversarial Example Defenses

Reference 93

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Attacking Graph Foundation Models Through Their Shared Representation Proceedings of the 35th International Conference on Machine Learning (ICML) , series =

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-01T15:05:40.788099Z

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Attacking Graph Foundation Models Through Their Shared Representation 2017 IEEE Symposium on Security and Privacy (S&P) , pages =

Reference 95

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Attacking Graph Foundation Models Through Their Shared Representation Proceedings of the 37th International Conference on Machine Learning (ICML) , series =

Reference 96

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Attacking Graph Foundation Models Through Their Shared Representation Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) , pages =

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-01T15:05:41.213024Z

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Attacking Graph Foundation Models Through Their Shared Representation Proceedings of The Web Conference 2020 (WWW) , pages =

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-01T15:05:41.343818Z

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Attacking Graph Foundation Models Through Their Shared Representation Proceedings of the 36th International Conference on Machine Learning (ICML) , series =

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-01T15:05:41.481117Z

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Observation bd6f487a-47b5-48d9-bf04-b42e8b7fbe16 · outbound

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Attacking Graph Foundation Models Through Their Shared Representation On Evaluating Adversarial Robustness of Large Vision-Language Models

Reference 100

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