{"total":25,"rows":[{"id":"2102.06966","title":"Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization","created":"2021-02-13","cited_by_count":70,"area":null},{"id":"2206.08702","title":"Sheaf Neural Networks with Connection Laplacians","created":"2022-06-17","cited_by_count":65,"area":null},{"id":"2112.13507","title":"Block Modeling-Guided Graph Convolutional Neural Networks","created":"2021-12-27","cited_by_count":32,"area":null},{"id":"2212.10822","title":"Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Neural Networks","created":"2022-12-21","cited_by_count":19,"area":null},{"id":"2204.01376","title":"Synthetic Graph Generation to Benchmark Graph Learning","created":"2022-04-04","cited_by_count":16,"area":null},{"id":"2103.10334","title":"Structure Inducing Pre-Training","created":"2021-03-18","cited_by_count":10,"area":null},{"id":"2310.10362","title":"Self-Pro: A Self-Prompt and Tuning Framework for Graph Neural Networks","created":"2023-10-16","cited_by_count":8,"area":null},{"id":"2205.11322","title":"Revisiting the role of heterophily in graph representation learning: An edge classification perspective","created":"2022-05-23","cited_by_count":6,"area":null},{"id":"2501.04570","title":"Large-Scale Spectral Graph Neural Networks via Laplacian Sparsification: Technical Report","created":"2025-01-08","cited_by_count":5,"area":null},{"id":"2105.03237","title":"Mini-batch graphs for robust image classification","created":"2021-04-22","cited_by_count":3,"area":null},{"id":"2105.04493","title":"Graph Feature Gating Networks","created":"2021-05-10","cited_by_count":3,"area":null},{"id":"2308.03306","title":"Implicit Graph Neural Diffusion Networks: Convergence, Generalization, and Over-Smoothing","created":"2023-08-07","cited_by_count":2,"area":null},{"id":"2410.20295","title":"DeCaf: A Causal Decoupling Framework for OOD Generalization on Node Classification","created":"2024-10-27","cited_by_count":2,"area":null},{"id":"2411.18905","title":"FedRGL: Robust Federated Graph Learning for Label Noise","created":"2024-11-28","cited_by_count":2,"area":null},{"id":"2209.15454","title":"GPNet: Simplifying Graph Neural Networks via Multi-channel Geometric Polynomials","created":"2022-09-30","cited_by_count":1,"area":null},{"id":"2210.16979","title":"When Do We Need Graph Neural Networks for Node Classification?","created":"2022-10-30","cited_by_count":1,"area":null},{"id":"2306.12640","title":"On Addressing the Limitations of Graph Neural Networks","created":"2023-06-22","cited_by_count":1,"area":null},{"id":"2401.09754","title":"Towards Robust Graph Structural Learning Beyond Homophily via Preserving Neighbor Similarity","created":"2024-01-18","cited_by_count":1,"area":null},{"id":"2509.00772","title":"Flow Matters: Directional and Expressive GNNs for Heterophilic Graphs","created":"2025-08-31","cited_by_count":1,"area":null},{"id":"2106.05455","title":"AKE-GNN: Effective Graph Learning with Adaptive Knowledge Exchange","created":"2021-06-10","cited_by_count":0,"area":null},{"id":"2212.14345","title":"On Learning the Structure of Clusters in Graphs","created":"2022-12-29","cited_by_count":0,"area":null},{"id":"2306.12943","title":"Evolving Computation Graphs","created":"2023-06-22","cited_by_count":0,"area":null},{"id":"2307.07956","title":"Automated Polynomial Filter Learning for Graph Neural Networks","created":"2023-07-16","cited_by_count":0,"area":null},{"id":"2404.01897","title":"Continuous Spiking Graph Neural Networks","created":"2024-04-02","cited_by_count":0,"area":null},{"id":"2411.07269","title":"Learning From Graph-Structured Data: Addressing Design Issues and Exploring Practical Applications in Graph Representation Learning","created":"2024-11-09","cited_by_count":0,"area":null}],"next_cursor":null}