{"total":196,"rows":[{"id":"2202.02466","title":"Handling Distribution Shifts on Graphs: An Invariance Perspective","created":"2022-02-05","cited_by_count":164,"area":null},{"id":"2204.08570","title":"A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability","created":"2022-04-18","cited_by_count":118,"area":null},{"id":"2110.14038","title":"Robustness of Graph Neural Networks at Scale","created":"2021-10-26","cited_by_count":100,"area":null},{"id":"2402.09154","title":"Attacking Large Language Models with Projected Gradient Descent","created":"2024-02-14","cited_by_count":94,"area":"alignment"},{"id":"2205.07424","title":"Trustworthy Graph Neural Networks: Aspects, Methods and Trends","created":"2022-05-16","cited_by_count":89,"area":null},{"id":"2107.04086","title":"Robust Counterfactual Explanations on Graph Neural Networks","created":"2021-07-08","cited_by_count":72,"area":null},{"id":"2210.03561","title":"Empowering Graph Representation Learning with Test-Time Graph Transformation","created":"2022-10-07","cited_by_count":67,"area":null},{"id":"2202.08057","title":"Understanding and Improving Graph Injection Attack by Promoting Unnoticeability","created":"2022-02-16","cited_by_count":62,"area":null},{"id":"2202.08871","title":"Graph Data Augmentation for Graph Machine Learning: A Survey","created":"2022-02-17","cited_by_count":62,"area":null},{"id":"2403.04468","title":"A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges","created":"2024-03-07","cited_by_count":54,"area":null},{"id":"2106.02820","title":"GraphMI: Extracting Private Graph Data from Graph Neural Networks","created":"2021-06-05","cited_by_count":50,"area":null},{"id":"2112.08331","title":"Model Stealing Attacks Against Inductive Graph Neural Networks","created":"2021-12-15","cited_by_count":50,"area":null},{"id":"2303.01263","title":"Unnoticeable Backdoor Attacks on Graph Neural Networks","created":"2023-02-11","cited_by_count":45,"area":null},{"id":"2403.07362","title":"Challenging Forgets: Unveiling the Worst-Case Forget Sets in Machine Unlearning","created":"2024-03-12","cited_by_count":44,"area":"safety"},{"id":"2306.01323","title":"Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?","created":"2023-06-02","cited_by_count":41,"area":null},{"id":"2202.06491","title":"Adversarial Graph Contrastive Learning with Information Regularization","created":"2022-02-14","cited_by_count":38,"area":null},{"id":"2301.13694","title":"Are Defenses for Graph Neural Networks Robust?","created":"2023-01-31","cited_by_count":38,"area":null},{"id":"2110.10942","title":"Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness","created":"2021-10-21","cited_by_count":35,"area":null},{"id":"2207.00012","title":"Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN","created":"2022-06-30","cited_by_count":34,"area":null},{"id":"2102.10454","title":"On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning","created":"2021-02-20","cited_by_count":29,"area":null},{"id":"2204.10037","title":"DropMessage: Unifying Random Dropping for Graph Neural Networks","created":"2022-04-21","cited_by_count":29,"area":null},{"id":"2102.09587","title":"Interpretable Stability Bounds for Spectral Graph Filters","created":"2021-02-18","cited_by_count":28,"area":null},{"id":"2306.15427","title":"Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions","created":"2023-06-27","cited_by_count":28,"area":null},{"id":"2108.09513","title":"A Hard Label Black-box Adversarial Attack Against Graph Neural Networks","created":"2021-08-21","cited_by_count":26,"area":null},{"id":"2205.13892","title":"EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks","created":"2022-05-27","cited_by_count":26,"area":null},{"id":"2209.07807","title":"Model Inversion Attacks against Graph Neural Networks","created":"2022-09-16","cited_by_count":26,"area":null},{"id":"2201.07986","title":"Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation","created":"2022-01-20","cited_by_count":25,"area":null},{"id":"2408.08685","title":"Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?","created":"2024-08-16","cited_by_count":23,"area":null},{"id":"2103.11882","title":"Generating Adversarial Computer Programs using Optimized Obfuscations","created":"2021-03-18","cited_by_count":22,"area":null},{"id":"2106.07767","title":"How does Heterophily Impact the Robustness of Graph Neural Networks? Theoretical Connections and Practical Implications","created":"2021-06-14","cited_by_count":21,"area":null},{"id":"2202.10107","title":"Model-Agnostic Augmentation for Accurate Graph Classification","created":"2022-02-21","cited_by_count":21,"area":null},{"id":"2212.04983","title":"Adversarial Weight Perturbation Improves Generalization in Graph Neural Networks","created":"2022-12-09","cited_by_count":21,"area":null},{"id":"2212.09254","title":"TextGrad: Advancing Robustness Evaluation in NLP by Gradient-Driven Optimization","created":"2022-12-19","cited_by_count":21,"area":null},{"id":"2106.10785","title":"Adversarial Attack on Graph Neural Networks as An Influence Maximization Problem","created":"2021-06-21","cited_by_count":20,"area":null},{"id":"2205.10014","title":"A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection","created":"2022-05-20","cited_by_count":20,"area":null},{"id":"2207.00425","title":"Transferable Graph Backdoor Attack","created":"2022-06-21","cited_by_count":19,"area":null},{"id":"2211.10782","title":"Let Graph be the Go Board: Gradient-free Node Injection Attack for Graph Neural Networks via Reinforcement Learning","created":"2022-11-19","cited_by_count":19,"area":null},{"id":"2305.00851","title":"Revisiting Robustness in Graph Machine Learning","created":"2023-05-01","cited_by_count":19,"area":null},{"id":"2104.09027","title":"Decentralized Inference with Graph Neural Networks in Wireless Communication Systems","created":"2021-04-19","cited_by_count":18,"area":null},{"id":"2108.03388","title":"Jointly Attacking Graph Neural Network and its Explanations","created":"2021-08-07","cited_by_count":18,"area":null},{"id":"2209.00793","title":"Structure-Preserving Graph Representation Learning","created":"2022-09-02","cited_by_count":17,"area":null},{"id":"2102.03716","title":"SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation","created":"2021-02-07","cited_by_count":16,"area":null},{"id":"2111.00684","title":"Graph Structural Attack by Perturbing Spectral Distance","created":"2021-11-01","cited_by_count":16,"area":null},{"id":"2210.07441","title":"Characterizing the Influence of Graph Elements","created":"2022-10-14","cited_by_count":16,"area":null},{"id":"2304.00010","title":"Towards Reasonable Budget Allocation in Untargeted Graph Structure Attacks via Gradient Debias","created":"2023-03-29","cited_by_count":16,"area":null},{"id":"2110.14855","title":"CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks","created":"2021-10-28","cited_by_count":15,"area":null},{"id":"2203.11200","title":"Exploiting Neighbor Effect: Conv-Agnostic GNNs Framework for Graphs with Heterophily","created":"2022-03-19","cited_by_count":15,"area":null},{"id":"2403.01467","title":"Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation","created":"2024-03-03","cited_by_count":15,"area":null},{"id":"2306.08604","title":"A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy","created":"2023-06-14","cited_by_count":14,"area":null},{"id":"2405.17984","title":"Cross-Context Backdoor Attacks against Graph Prompt Learning","created":"2024-05-28","cited_by_count":14,"area":"safety"},{"id":"2106.11865","title":"NetFense: Adversarial Defenses against Privacy Attacks on Neural Networks for Graph Data","created":"2021-06-22","cited_by_count":13,"area":null},{"id":"2306.10683","title":"Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation","created":"2023-06-19","cited_by_count":13,"area":null},{"id":"2203.12852","title":"Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges","created":"2022-03-23","cited_by_count":12,"area":null},{"id":"2209.05957","title":"Adversarial Inter-Group Link Injection Degrades the Fairness of Graph Neural Networks","created":"2022-09-13","cited_by_count":12,"area":null},{"id":"2211.10896","title":"Spectral Adversarial Training for Robust Graph Neural Network","created":"2022-11-20","cited_by_count":12,"area":null},{"id":"2402.13987","title":"A Simple and Yet Fairly Effective Defense for Graph Neural Networks","created":"2024-02-21","cited_by_count":12,"area":null},{"id":"2202.07201","title":"Holistic Adversarial Robustness of Deep Learning Models","created":"2022-02-15","cited_by_count":11,"area":null},{"id":"2405.11034","title":"Safety in Graph Machine Learning: Threats and Safeguards","created":"2024-05-17","cited_by_count":11,"area":null},{"id":"2407.12068","title":"Learning on Graphs with Large Language Models(LLMs): A Deep Dive into Model Robustness","created":"2024-07-16","cited_by_count":11,"area":null},{"id":"2105.12419","title":"Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge","created":"2021-05-26","cited_by_count":10,"area":null},{"id":"2202.07114","title":"Recent Advances in Reliable Deep Graph Learning: Inherent Noise, Distribution Shift, and Adversarial Attack","created":"2022-02-15","cited_by_count":10,"area":null},{"id":"2208.05514","title":"Are Gradients on Graph Structure Reliable in Gray-box Attacks?","created":"2022-08-07","cited_by_count":10,"area":null},{"id":"2301.02039","title":"Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks","created":"2023-01-05","cited_by_count":10,"area":null},{"id":"2402.13937","title":"Verifying message-passing neural networks via topology-based bounds tightening","created":"2024-02-21","cited_by_count":10,"area":null},{"id":"2105.08007","title":"Understanding and Improvement of Adversarial Training for Network Embedding from an Optimization Perspective","created":"2021-05-17","cited_by_count":9,"area":null},{"id":"2205.03546","title":"Bandits for Structure Perturbation-based Black-box Attacks to Graph Neural Networks with Theoretical Guarantees","created":"2022-05-07","cited_by_count":9,"area":null},{"id":"2303.09767","title":"It Is All About Data: A Survey on the Effects of Data on Adversarial Robustness","created":"2023-03-17","cited_by_count":9,"area":null},{"id":"2308.10779","title":"Spear and Shield: Adversarial Attacks and Defense Methods for Model-Based Link Prediction on Continuous-Time Dynamic Graphs","created":"2023-08-21","cited_by_count":9,"area":null},{"id":"2404.17947","title":"Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature Attacks","created":"2024-04-27","cited_by_count":9,"area":null},{"id":"2405.16405","title":"Intruding with Words: Towards Understanding Graph Injection Attacks at the Text Level","created":"2024-05-26","cited_by_count":9,"area":null},{"id":"2107.11327","title":"Structack: Structure-based Adversarial Attacks on Graph Neural Networks","created":"2021-07-23","cited_by_count":8,"area":null},{"id":"2208.06651","title":"Revisiting Adversarial Attacks on Graph Neural Networks for Graph Classification","created":"2022-08-13","cited_by_count":8,"area":null},{"id":"2308.07834","title":"Simple and Efficient Partial Graph Adversarial Attack: A New Perspective","created":"2023-08-15","cited_by_count":8,"area":null},{"id":"2311.14934","title":"Robust Graph Neural Networks via Unbiased Aggregation","created":"2023-11-25","cited_by_count":8,"area":null},{"id":"2412.08160","title":"DG-Mamba: Robust and Efficient Dynamic Graph Structure Learning with Selective State Space Models","created":"2024-12-11","cited_by_count":8,"area":"ssm"},{"id":"2201.06202","title":"Neighboring Backdoor Attacks on Graph Convolutional Network","created":"2022-01-17","cited_by_count":7,"area":null},{"id":"2210.13815","title":"FocusedCleaner: Sanitizing Poisoned Graphs for Robust GNN-based Node Classification","created":"2022-10-25","cited_by_count":7,"area":null},{"id":"2104.10586","title":"Mixture of Robust Experts (MoRE):A Robust Denoising Method towards multiple perturbations","created":"2021-04-21","cited_by_count":6,"area":null},{"id":"2306.13854","title":"Similarity Preserving Adversarial Graph Contrastive Learning","created":"2023-06-24","cited_by_count":6,"area":null},{"id":"2310.15653","title":"Deceptive Fairness Attacks on Graphs via Meta Learning","created":"2023-10-24","cited_by_count":6,"area":null},{"id":"2401.09945","title":"HGAttack: Transferable Heterogeneous Graph Adversarial Attack","created":"2024-01-18","cited_by_count":6,"area":null},{"id":"2403.02723","title":"Minimum Topology Attacks for Graph Neural Networks","created":"2024-03-05","cited_by_count":6,"area":null},{"id":"2406.05482","title":"Efficient Topology-aware Data Augmentation for High-Degree Graph Neural Networks","created":"2024-06-08","cited_by_count":6,"area":null},{"id":"2104.09369","title":"Adversarial Diffusion Attacks on Graph-based Traffic Prediction Models","created":"2021-04-19","cited_by_count":5,"area":null},{"id":"2112.13267","title":"Task and Model Agnostic Adversarial Attack on Graph Neural Networks","created":"2021-12-25","cited_by_count":5,"area":null},{"id":"2208.06956","title":"ARIEL: Adversarial Graph Contrastive Learning","created":"2022-08-15","cited_by_count":5,"area":null},{"id":"2212.04656","title":"Robust Graph Representation Learning via Predictive Coding","created":"2022-12-09","cited_by_count":5,"area":null},{"id":"2305.02383","title":"On the Security Risks of Knowledge Graph Reasoning","created":"2023-05-03","cited_by_count":5,"area":null},{"id":"2310.03312","title":"Certifiably Robust Graph Contrastive Learning","created":"2023-10-05","cited_by_count":5,"area":null},{"id":"2401.15203","title":"FedGT: Federated Node Classification with Scalable Graph Transformer","created":"2024-01-26","cited_by_count":5,"area":null},{"id":"2405.15564","title":"Rethinking Independent Cross-Entropy Loss For Graph-Structured Data","created":"2024-05-24","cited_by_count":5,"area":null},{"id":"2406.06417","title":"Explainable Graph Neural Networks Under Fire","created":"2024-06-10","cited_by_count":5,"area":null},{"id":"2410.23182","title":"ProTransformer: Robustify Transformers via Plug-and-Play Paradigm","created":"2024-10-30","cited_by_count":5,"area":null},{"id":"2110.10482","title":"Surrogate Representation Learning with Isometric Mapping for Gray-box Graph Adversarial Attacks","created":"2021-10-20","cited_by_count":4,"area":null},{"id":"2201.08557","title":"Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective","created":"2022-01-21","cited_by_count":4,"area":null},{"id":"2201.13025","title":"Learning Robust Representation through Graph Adversarial Contrastive Learning","created":"2022-01-31","cited_by_count":4,"area":null},{"id":"2212.13607","title":"EDoG: Adversarial Edge Detection For Graph Neural Networks","created":"2022-12-27","cited_by_count":4,"area":null},{"id":"2303.11376","title":"GNN-Ensemble: Towards Random Decision Graph Neural Networks","created":"2023-03-20","cited_by_count":4,"area":null},{"id":"2310.13822","title":"Adversarial Attacks on Fairness of Graph Neural Networks","created":"2023-10-20","cited_by_count":4,"area":null},{"id":"2402.08653","title":"SAGMAN: Stability Analysis of Graph Neural Networks on the Manifolds","created":"2024-02-13","cited_by_count":4,"area":null}],"next_cursor":"100"}