{"total":21,"rows":[{"id":"2202.07082","title":"Graph Neural Networks for Graphs with Heterophily: A Survey","created":"2022-02-14","cited_by_count":175,"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":"2305.16780","title":"Graph Neural Convection-Diffusion with Heterophily","created":"2023-05-26","cited_by_count":26,"area":null},{"id":"2302.06299","title":"Homophily-oriented Heterogeneous Graph Rewiring","created":"2023-02-13","cited_by_count":18,"area":null},{"id":"2302.08048","title":"Robust Mid-Pass Filtering Graph Convolutional Networks","created":"2023-02-16","cited_by_count":17,"area":null},{"id":"2401.09769","title":"A Survey on Learning from Graphs with Heterophily: Recent Advances and Future Directions","created":"2024-01-18","cited_by_count":13,"area":null},{"id":"2409.17386","title":"Beyond Redundancy: Information-aware Unsupervised Multiplex Graph Structure Learning","created":"2024-09-25","cited_by_count":12,"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":"2403.17351","title":"Learn from Heterophily: Heterophilous Information-enhanced Graph Neural Network","created":"2024-03-26","cited_by_count":3,"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":"2405.19919","title":"Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning","created":"2024-05-30","cited_by_count":2,"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":"2411.18648","title":"MADE: Graph Backdoor Defense with Masked Unlearning","created":"2024-11-26","cited_by_count":1,"area":null},{"id":"2307.12555","title":"Robust Graph Contrastive Learning with Information Restoration","created":"2023-07-24","cited_by_count":0,"area":null},{"id":"2404.16366","title":"Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection","created":"2024-04-25","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":"2504.19785","title":"Heterophily-informed Message Passing","created":"2025-04-28","cited_by_count":0,"area":null},{"id":"2506.13276","title":"Navigating the Black Box: Leveraging LLMs for Effective Text-Level Graph Injection Attacks","created":"2025-06-16","cited_by_count":0,"area":null},{"id":"2511.00083","title":"Fixed-point graph convolutional networks against adversarial attacks","created":"2025-10-29","cited_by_count":0,"area":null},{"id":"2604.27387","title":"Robust Learning on Heterogeneous Graphs with Heterophily: A Graph Structure Learning Approach","created":"2026-04-30","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}],"next_cursor":null}