{"total":98,"rows":[{"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":"2205.07424","title":"Trustworthy Graph Neural Networks: Aspects, Methods and Trends","created":"2022-05-16","cited_by_count":89,"area":null},{"id":"2304.05055","title":"A Comprehensive Survey on Deep Graph Representation Learning","created":"2023-04-11","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":"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":"2301.13694","title":"Are Defenses for Graph Neural Networks Robust?","created":"2023-01-31","cited_by_count":38,"area":null},{"id":"2404.14928","title":"Graph Machine Learning in the Era of Large Language Models (LLMs)","created":"2024-04-23","cited_by_count":33,"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":"2310.06396","title":"Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach","created":"2023-10-10","cited_by_count":24,"area":null},{"id":"2401.11720","title":"Graph Condensation: A Survey","created":"2024-01-22","cited_by_count":23,"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":"2301.00896","title":"Efficient Robustness Assessment via Adversarial Spatial-Temporal Focus on Videos","created":"2023-01-03","cited_by_count":20,"area":null},{"id":"2305.00851","title":"Revisiting Robustness in Graph Machine Learning","created":"2023-05-01","cited_by_count":19,"area":null},{"id":"2410.13798","title":"Learning Graph Quantized Tokenizers","created":"2024-10-17","cited_by_count":19,"area":null},{"id":"2310.02902","title":"Searching for High-Value Molecules Using Reinforcement Learning and Transformers","created":"2023-10-04","cited_by_count":18,"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":"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":"2201.12741","title":"GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks","created":"2022-01-30","cited_by_count":14,"area":null},{"id":"2202.12993","title":"Projective Ranking-based GNN Evasion Attacks","created":"2022-02-25","cited_by_count":14,"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":"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":"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":"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":"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":"2410.06976","title":"Matcha: Mitigating Graph Structure Shifts with Test-Time Adaptation","created":"2024-10-09","cited_by_count":10,"area":null},{"id":"2206.05070","title":"Fundamental Limits in Formal Verification of Message-Passing Neural Networks","created":"2022-06-10","cited_by_count":9,"area":null},{"id":"2210.08906","title":"A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities","created":"2022-10-17","cited_by_count":9,"area":null},{"id":"2401.04331","title":"Coupling Graph Neural Networks with Fractional Order Continuous Dynamics: A Robustness Study","created":"2024-01-09","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":"2410.04968","title":"Collaboration! Towards Robust Neural Methods for Routing Problems","created":"2024-10-07","cited_by_count":9,"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":"2406.13200","title":"RobGC: Towards Robust Graph Condensation","created":"2024-06-19","cited_by_count":8,"area":null},{"id":"2406.16715","title":"GC4NC: A Benchmark Framework for Graph Condensation on Node Classification with New Insights","created":"2024-06-24","cited_by_count":7,"area":null},{"id":"2505.16284","title":"Only Large Weights (And Not Skip Connections) Can Prevent the Perils of Rank Collapse","created":"2025-05-22","cited_by_count":7,"area":null},{"id":"2403.02723","title":"Minimum Topology Attacks for Graph Neural Networks","created":"2024-03-05","cited_by_count":6,"area":null},{"id":"2310.03312","title":"Certifiably Robust Graph Contrastive Learning","created":"2023-10-05","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":"2407.12421","title":"SafePowerGraph: Safety-aware Evaluation of Graph Neural Networks for Transmission Power Grids","created":"2024-07-17","cited_by_count":5,"area":null},{"id":"2409.00055","title":"SORSA: Singular Values and Orthonormal Regularized Singular Vectors Adaptation of Large Language Models","created":"2024-08-21","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":"2506.11844","title":"TrustGLM: Evaluating the Robustness of GraphLLMs Against Prompt, Text, and Structure Attacks","created":"2025-06-13","cited_by_count":5,"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":"2208.01853","title":"Robust Graph Neural Networks using Weighted Graph Laplacian","created":"2022-08-03","cited_by_count":4,"area":null},{"id":"2305.15792","title":"IDEA: Invariant Defense for Graph Adversarial Robustness","created":"2023-05-25","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":"2404.14642","title":"Uncertainty Quantification on Graph Learning: A Survey","created":"2024-04-23","cited_by_count":4,"area":null},{"id":"2411.19440","title":"Gradient Inversion Attack on Graph Neural Networks","created":"2024-11-29","cited_by_count":4,"area":null},{"id":"2502.12618","title":"Uncertainty-Aware Graph Structure Learning","created":"2025-02-18","cited_by_count":4,"area":null},{"id":"2508.09320","title":"Exact Verification of Graph Neural Networks with Incremental Constraint Solving","created":"2025-08-12","cited_by_count":4,"area":null},{"id":"2603.26105","title":"Are LLM-Enhanced Graph Neural Networks Robust against Poisoning Attacks?","created":"2026-03-27","cited_by_count":4,"area":null},{"id":"2502.05000","title":"Robust Graph Learning Against Adversarial Evasion Attacks via Prior-Free Diffusion-Based Structure Purification","created":"2025-02-07","cited_by_count":3,"area":null},{"id":"2506.04694","title":"Influence Functions for Edge Edits in Non-Convex Graph Neural Networks","created":"2025-06-05","cited_by_count":3,"area":null},{"id":"2204.09803","title":"GUARD: Graph Universal Adversarial Defense","created":"2022-04-20","cited_by_count":2,"area":null},{"id":"2208.12815","title":"What Does the Gradient Tell When Attacking the Graph Structure","created":"2022-08-26","cited_by_count":2,"area":null},{"id":"2308.15614","title":"Everything Perturbed All at Once: Enabling Differentiable Graph Attacks","created":"2023-08-29","cited_by_count":2,"area":null},{"id":"2311.17853","title":"On the Adversarial Robustness of Graph Contrastive Learning Methods","created":"2023-11-29","cited_by_count":2,"area":null},{"id":"2406.03833","title":"Talos: A More Effective and Efficient Adversarial Defense for GNN Models Based on the Global Homophily of Graphs","created":"2024-06-06","cited_by_count":2,"area":null},{"id":"2409.14161","title":"When Witnesses Defend: A Witness Graph Topological Layer for Adversarial Graph Learning","created":"2024-09-21","cited_by_count":2,"area":null},{"id":"2410.05707","title":"Network Topology Inference from Smooth Signals Under Partial Observability","created":"2024-10-08","cited_by_count":2,"area":null},{"id":"2412.00789","title":"A Cognac Shot To Forget Bad Memories: Corrective Unlearning for Graph Neural Networks","created":"2024-12-01","cited_by_count":2,"area":null},{"id":"2602.06240","title":"ATEX-CF: Attack-Informed Counterfactual Explanations for Graph Neural Networks","created":"2026-02-05","cited_by_count":2,"area":null},{"id":"2605.03514","title":"Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding","created":"2026-05-05","cited_by_count":2,"area":null},{"id":"2306.02002","title":"Can Directed Graph Neural Networks be Adversarially Robust?","created":"2023-06-03","cited_by_count":1,"area":null},{"id":"2311.06942","title":"Resilient Graph Neural Networks: A Coupled Dynamical Systems Approach","created":"2023-11-12","cited_by_count":1,"area":null},{"id":"2312.06568","title":"Sparse but Strong: Crafting Adversarially Robust Graph Lottery Tickets","created":"2023-12-11","cited_by_count":1,"area":null},{"id":"2406.13920","title":"Understanding the Robustness of Graph Neural Networks against Adversarial Attacks","created":"2024-06-20","cited_by_count":1,"area":null},{"id":"2407.11764","title":"Adversarial Robustness of Graph Transformers","created":"2024-07-16","cited_by_count":1,"area":null},{"id":"2412.18886","title":"Adversarial Training for Graph Neural Networks via Graph Subspace Energy Optimization","created":"2024-12-25","cited_by_count":1,"area":null},{"id":"2506.08401","title":"Single-Node Trigger Backdoor Attacks in Graph-Based Recommendation Systems","created":"2025-06-10","cited_by_count":1,"area":null},{"id":"2510.17185","title":"Robustness in Text-Attributed Graph Learning: Insights, Trade-offs, and New Defenses","created":"2025-10-20","cited_by_count":1,"area":null},{"id":"2602.00318","title":"Optimal Transport-Guided Adversarial Attacks on Graph Neural Network-Based Bot Detection","created":"2026-01-30","cited_by_count":1,"area":null},{"id":"2603.18570","title":"Attack by Unlearning: Unlearning-Induced Adversarial Attacks on Graph Neural Networks","created":"2026-03-19","cited_by_count":1,"area":null},{"id":"2209.08064","title":"A Systematic Evaluation of Node Embedding Robustness","created":"2022-09-16","cited_by_count":0,"area":null},{"id":"2312.05502","title":"Poisoning $\\times$ Evasion: Symbiotic Adversarial Robustness for Graph Neural Networks","created":"2023-12-09","cited_by_count":0,"area":null},{"id":"2403.09901","title":"Robust Subgraph Learning by Monitoring Early Training Representations","created":"2024-03-14","cited_by_count":0,"area":null},{"id":"2403.12399","title":"Dynamic Gradient Influencing for Viral Marketing Using Graph Neural Networks","created":"2024-03-19","cited_by_count":0,"area":null},{"id":"2406.02059","title":"Graph Adversarial Diffusion Convolution","created":"2024-06-04","cited_by_count":0,"area":null},{"id":"2408.16537","title":"SFR-GNN: Simple and Fast Robust GNNs against Structural Attacks","created":"2024-08-29","cited_by_count":0,"area":null},{"id":"2412.05883","title":"On the Adversarial Robustness of Graph Neural Networks with Graph Reduction","created":"2024-12-08","cited_by_count":0,"area":null},{"id":"2503.20630","title":"$\\beta$-GNN: A Robust Ensemble Approach Against Graph Structure Perturbation","created":"2025-03-26","cited_by_count":0,"area":null},{"id":"2504.20869","title":"Quantifying the Noise of Structural Perturbations on Graph Adversarial Attacks","created":"2025-04-29","cited_by_count":0,"area":null},{"id":"2506.11413","title":"Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates","created":"2025-06-13","cited_by_count":0,"area":null},{"id":"2506.11611","title":"KCES: Training-Free Defense for Robust Graph Neural Networks via Kernel Complexity","created":"2025-06-13","cited_by_count":0,"area":null},{"id":"2507.05540","title":"Robust Learning on Noisy Graphs via Latent Space Constraints with External Knowledge","created":"2025-07-07","cited_by_count":0,"area":null},{"id":"2507.08212","title":"EvA: Evolutionary Attacks on Graphs","created":"2025-07-10","cited_by_count":0,"area":null},{"id":"2507.18804","title":"Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors","created":"2025-07-24","cited_by_count":0,"area":null},{"id":"2509.05681","title":"SEASONED: Semantic-Enhanced Self-Counterfactual Explainable Detection of Adversarial Exploiter Contracts","created":"2025-09-06","cited_by_count":0,"area":null},{"id":"2510.22643","title":"Enhancing Graph Classification Robustness with Singular Pooling","created":"2025-10-26","cited_by_count":0,"area":null},{"id":"2511.06216","title":"Adaptive Multi-view Graph Contrastive Learning via Fractional-order Neural Diffusion Networks","created":"2025-11-09","cited_by_count":0,"area":null},{"id":"2511.12423","title":"GRAPHTEXTACK: A Realistic Black-Box Node Injection Attack on LLM-Enhanced GNNs","created":"2025-11-16","cited_by_count":0,"area":null},{"id":"2603.26136","title":"PEANUT: Perturbations by Eigenvector Alignment for Attacking Graph Neural Networks Under Topology-Driven Message Passing","created":"2026-03-27","cited_by_count":0,"area":null},{"id":"2605.05534","title":"Adversarial Graph Neural Network Benchmarks: Towards Practical and Fair Evaluation","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2605.23239","title":"Self-supervised Adversarial Purification for Graph Neural Networks","created":"2026-05-22","cited_by_count":0,"area":null},{"id":"2608.29054","title":"Let Prompts Bridge Defense Knowledge: Transferable Graph Purification via Vulnerability-Aware GPL","created":"2026-08-29","cited_by_count":0,"area":null},{"id":"2609.09698","title":"Kernel-Complexity Edge Sanitization for Training-Free Defense against Structural Graph Attacks","created":"2026-09-09","cited_by_count":0,"area":null}],"next_cursor":null}