{"total":26,"rows":[{"id":"2104.13478","title":"Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges","created":"2021-04-27","cited_by_count":1167,"area":null},{"id":"2110.07875","title":"Graph Neural Networks with Learnable Structural and Positional Representations","created":"2021-10-15","cited_by_count":333,"area":null},{"id":"2107.01188","title":"Combinatorial Optimization with Physics-Inspired Graph Neural Networks","created":"2021-07-02","cited_by_count":108,"area":null},{"id":"2108.01938","title":"PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equations","created":"2021-08-04","cited_by_count":106,"area":null},{"id":"2212.13350","title":"A Generalization of ViT/MLP-Mixer to Graphs","created":"2022-12-27","cited_by_count":93,"area":null},{"id":"2102.10062","title":"A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective","created":"2021-02-19","cited_by_count":38,"area":null},{"id":"2202.01606","title":"Graph Coloring with Physics-Inspired Graph Neural Networks","created":"2022-02-03","cited_by_count":29,"area":null},{"id":"2107.04894","title":"Improving Inductive Link Prediction Using Hyper-Relational Facts","created":"2021-07-10","cited_by_count":22,"area":null},{"id":"2105.10488","title":"Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery","created":"2021-05-17","cited_by_count":18,"area":null},{"id":"2203.12852","title":"Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges","created":"2022-03-23","cited_by_count":12,"area":null},{"id":"2112.06567","title":"Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphs","created":"2021-12-13","cited_by_count":11,"area":null},{"id":"2401.03350","title":"Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks","created":"2024-01-07","cited_by_count":8,"area":null},{"id":"2410.06746","title":"Cluster-wise Graph Transformer with Dual-granularity Kernelized Attention","created":"2024-10-09","cited_by_count":8,"area":null},{"id":"2303.13775","title":"GSplit: Scaling Graph Neural Network Training on Large Graphs via Split-Parallelism","created":"2023-03-24","cited_by_count":6,"area":null},{"id":"2303.00613","title":"Diffusing Graph Attention","created":"2023-03-01","cited_by_count":5,"area":null},{"id":"2310.05296","title":"Tailoring Self-Attention for Graph via Rooted Subtrees","created":"2023-10-08","cited_by_count":5,"area":null},{"id":"2109.10119","title":"mGNN: Generalizing the Graph Neural Networks to the Multilayer Case","created":"2021-09-21","cited_by_count":4,"area":null},{"id":"2304.05498","title":"GraphGANFed: A Federated Generative Framework for Graph-Structured Molecules Towards Efficient Drug Discovery","created":"2023-04-11","cited_by_count":4,"area":null},{"id":"2105.01171","title":"Machine Learning Applications for Therapeutic Tasks with Genomics Data","created":"2021-05-03","cited_by_count":3,"area":null},{"id":"2407.19420","title":"UniGAP: A Universal and Adaptive Graph Upsampling Approach to Mitigate Over-Smoothing in Node Classification Tasks","created":"2024-07-28","cited_by_count":3,"area":null},{"id":"2110.05478","title":"An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design","created":"2021-10-10","cited_by_count":1,"area":null},{"id":"2108.13051","title":"Demystifying Drug Repurposing Domain Comprehension with Knowledge Graph Embedding","created":"2021-08-30","cited_by_count":0,"area":null},{"id":"2205.00354","title":"Graph Anisotropic Diffusion","created":"2022-04-30","cited_by_count":0,"area":null},{"id":"2305.10257","title":"Improving Link Prediction in Social Networks Using Local and Global Features: A Clustering-based Approach","created":"2023-05-17","cited_by_count":0,"area":null},{"id":"2309.10976","title":"Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks","created":"2023-09-20","cited_by_count":0,"area":null},{"id":"2505.19445","title":"MetaGMT: Improving Actionable Interpretability of Graph Multilinear Networks via Meta-Learning Filtration","created":"2025-05-26","cited_by_count":0,"area":null}],"next_cursor":null}