{"total":43,"rows":[{"id":"2102.07835","title":"Topological Graph Neural Networks","created":"2021-02-15","cited_by_count":87,"area":null},{"id":"2212.04450","title":"GAUCHE: A Library for Gaussian Processes in Chemistry","created":"2022-12-06","cited_by_count":41,"area":null},{"id":"2306.05955","title":"Path Neural Networks: Expressive and Accurate Graph Neural Networks","created":"2023-06-09","cited_by_count":32,"area":null},{"id":"2508.00716","title":"Nested Graph Pseudo-Label Refinement for Noisy Label Domain Adaptation Learning","created":"2025-08-01","cited_by_count":10,"area":null},{"id":"2210.09535","title":"Graph Anomaly Detection with Unsupervised GNNs","created":"2022-10-18","cited_by_count":8,"area":null},{"id":"2210.16844","title":"Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoders","created":"2022-10-30","cited_by_count":8,"area":null},{"id":"2211.01689","title":"Isotropic Gaussian Processes on Finite Spaces of Graphs","created":"2022-11-03","cited_by_count":7,"area":null},{"id":"2302.00942","title":"Efficient Graph Field Integrators Meet Point Clouds","created":"2023-02-02","cited_by_count":7,"area":null},{"id":"2402.03838","title":"Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels","created":"2024-02-06","cited_by_count":7,"area":null},{"id":"2201.12064","title":"Multiscale Graph Comparison via the Embedded Laplacian Discrepancy","created":"2022-01-28","cited_by_count":5,"area":null},{"id":"2202.11028","title":"Generating Synthetic Mobility Networks with Generative Adversarial Networks","created":"2022-02-22","cited_by_count":5,"area":null},{"id":"2402.06087","title":"Descriptive Kernel Convolution Network with Improved Random Walk Kernel","created":"2024-02-08","cited_by_count":5,"area":null},{"id":"2207.04216","title":"Wasserstein Graph Distance Based on $L_1$-Approximated Tree Edit Distance between Weisfeiler-Lehman Subtrees","created":"2022-07-09","cited_by_count":4,"area":null},{"id":"2306.03770","title":"Graph Classification Gaussian Processes via Spectral Features","created":"2023-06-06","cited_by_count":4,"area":null},{"id":"2308.15283","title":"Structural Node Embeddings with Homomorphism Counts","created":"2023-08-29","cited_by_count":4,"area":null},{"id":"2506.22621","title":"Modeling Hierarchical Spaces: A Review and Unified Framework for Surrogate-Based Architecture Design","created":"2025-06-27","cited_by_count":4,"area":null},{"id":"2507.22524","title":"HGCN(O): A Self-Tuning GCN HyperModel Toolkit for Outcome Prediction in Event-Sequence Data","created":"2025-07-30","cited_by_count":4,"area":null},{"id":"2207.02760","title":"TREE-G: Decision Trees Contesting Graph Neural Networks","created":"2022-07-06","cited_by_count":3,"area":null},{"id":"2406.15881","title":"Fast Tree-Field Integrators: From Low Displacement Rank to Topological Transformers","created":"2024-06-22","cited_by_count":3,"area":null},{"id":"2410.10368","title":"Optimal Time Complexity Algorithms for Computing General Random Walk Graph Kernels on Sparse Graphs","created":"2024-10-14","cited_by_count":3,"area":null},{"id":"2505.23640","title":"Global optimization of graph acquisition functions for neural architecture search","created":"2025-05-29","cited_by_count":2,"area":null},{"id":"2510.07716","title":"Computationally-efficient Graph Modeling with Refined Graph Random Features","created":"2025-10-09","cited_by_count":2,"area":null},{"id":"2401.04250","title":"Explaining the Power of Topological Data Analysis in Graph Machine Learning","created":"2024-01-08","cited_by_count":1,"area":null},{"id":"2401.11609","title":"Graph Edits for Counterfactual Explanations: A comparative study","created":"2024-01-21","cited_by_count":1,"area":null},{"id":"2410.15721","title":"Learning signals defined on graphs with optimal transport and Gaussian process regression","created":"2024-10-21","cited_by_count":1,"area":null},{"id":"2410.23748","title":"Exploring Consistency in Graph Representations:from Graph Kernels to Graph Neural Networks","created":"2024-10-31","cited_by_count":1,"area":null},{"id":"2502.00737","title":"Scalable Sobolev IPM for Probability Measures on a Graph","created":"2025-02-02","cited_by_count":1,"area":null},{"id":"2505.12129","title":"Metric Graph Kernels via the Tropical Torelli Map","created":"2025-05-17","cited_by_count":1,"area":null},{"id":"2509.17693","title":"Fast, Accurate and Interpretable Graph Classification with Topological Kernels","created":"2025-09-22","cited_by_count":1,"area":null},{"id":"2106.15239","title":"Generating the Graph Gestalt: Kernel-Regularized Graph Representation Learning","created":"2021-06-29","cited_by_count":0,"area":null},{"id":"2202.03884","title":"GraphDCA -- a Framework for Node Distribution Comparison in Real and Synthetic Graphs","created":"2022-02-08","cited_by_count":0,"area":null},{"id":"2203.15009","title":"DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series","created":"2022-03-28","cited_by_count":0,"area":null},{"id":"2205.15555","title":"Graph-level Neural Networks: Current Progress and Future Directions","created":"2022-05-31","cited_by_count":0,"area":null},{"id":"2405.09247","title":"Graph Neural Network based Handwritten Trajectories Recognition","created":"2024-05-15","cited_by_count":0,"area":null},{"id":"2405.18068","title":"A Survey of Latent Factor Models in Recommender Systems","created":"2024-05-28","cited_by_count":0,"area":null},{"id":"2406.11901","title":"A Deep Learning Framework for Evaluating Dynamic Network Generative Models and Anomaly Detection","created":"2024-06-15","cited_by_count":0,"area":null},{"id":"2409.00338","title":"GSpect: Spectral Filtering for Cross-Scale Graph Classification","created":"2024-08-31","cited_by_count":0,"area":null},{"id":"2502.00739","title":"An Efficient Orlicz-Sobolev Approach for Transporting Unbalanced Measures on a Graph","created":"2025-02-02","cited_by_count":0,"area":null},{"id":"2503.11046","title":"Measuring Similarity in Causal Graphs: A Framework for Semantic and Structural Analysis","created":"2025-03-14","cited_by_count":0,"area":null},{"id":"2507.03560","title":"Simplifying Graph Kernels for Efficient","created":"2025-07-04","cited_by_count":0,"area":null},{"id":"2605.06466","title":"Diversity Curves for Graph Representation Learning","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2609.19083","title":"A General Kernel Framework for Non-CND Distance Measures Using |D|-Dimensional Sparse Landmark Embeddings","created":"2026-09-16","cited_by_count":0,"area":null},{"id":"2610.12946","title":"From Imbalance to Evidence: Deep Nonparametric Testing for Complex Data under Severe Sample Imbalance","created":"2026-10-09","cited_by_count":0,"area":null}],"next_cursor":null}