{"total":18,"rows":[{"id":"2310.07235","title":"Are GATs Out of Balance?","created":"2023-10-11","cited_by_count":10,"area":null},{"id":"2408.03669","title":"Beyond Over-smoothing: Uncovering the Trainability Challenges in Deep Graph Neural Networks","created":"2024-08-07","cited_by_count":10,"area":null},{"id":"2409.08676","title":"Redesigning graph filter-based GNNs to relax the homophily assumption","created":"2024-09-13","cited_by_count":8,"area":null},{"id":"2403.01475","title":"Representation Learning on Heterophilic Graph with Directional Neighborhood Attention","created":"2024-03-03","cited_by_count":6,"area":null},{"id":"2409.11504","title":"Preventing Representational Rank Collapse in MPNNs by Splitting the Computational Graph","created":"2024-09-17","cited_by_count":6,"area":null},{"id":"2505.23185","title":"Improving the Effective Receptive Field of Message-Passing Neural Networks","created":"2025-05-29","cited_by_count":6,"area":null},{"id":"2406.00418","title":"GATE: How to Keep Out Intrusive Neighbors","created":"2024-06-01","cited_by_count":5,"area":null},{"id":"2409.05100","title":"MaxCutPool: differentiable feature-aware Maxcut for pooling in graph neural networks","created":"2024-09-08","cited_by_count":5,"area":null},{"id":"2409.09888","title":"Flexible Diffusion Scopes with Parameterized Laplacian for Heterophilic Graph Learning","created":"2024-09-15","cited_by_count":3,"area":null},{"id":"2404.04874","title":"Quadratic Binary Optimization with Graph Neural Networks","created":"2024-04-07","cited_by_count":2,"area":null},{"id":"2412.09805","title":"Making Classic GNNs Strong Baselines Across Varying Homophily: A Smoothness-Generalization Perspective","created":"2024-12-13","cited_by_count":2,"area":null},{"id":"2502.11394","title":"A Signed Graph Approach to Understanding and Mitigating Oversmoothing in GNNs","created":"2025-02-17","cited_by_count":2,"area":null},{"id":"2403.15077","title":"GTAGCN: Generalized Topology Adaptive Graph Convolutional Networks","created":"2024-03-22","cited_by_count":1,"area":null},{"id":"2404.03081","title":"First-order PDES for Graph Neural Networks: Advection And Burgers Equation Models","created":"2024-04-03","cited_by_count":0,"area":null},{"id":"2501.14964","title":"Personalized Layer Selection for Graph Neural Networks","created":"2025-01-24","cited_by_count":0,"area":null},{"id":"2504.13426","title":"Simplifying Graph Convolutional Networks with Redundancy-Free Neighbors","created":"2025-04-18","cited_by_count":0,"area":null},{"id":"2601.01123","title":"Learning from Historical Activations in Graph Neural Networks","created":"2026-01-03","cited_by_count":0,"area":null},{"id":"2606.01660","title":"Gate the Filter, Not the Message: Node-Channel Mixtures for Pre-Propagation GNNs","created":"2026-06-01","cited_by_count":0,"area":null}],"next_cursor":null}