{"total":64,"rows":[{"id":"2307.03759","title":"A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection","created":"2023-07-07","cited_by_count":145,"area":null},{"id":"2307.01026","title":"Temporal Graph Benchmark for Machine Learning on Temporal Graphs","created":"2023-07-03","cited_by_count":138,"area":null},{"id":"2207.10128","title":"Towards Better Evaluation for Dynamic Link Prediction","created":"2022-07-20","cited_by_count":122,"area":null},{"id":"2407.01290","title":"Hypformer: Exploring Efficient Transformer Fully in Hyperbolic Space","created":"2024-07-01","cited_by_count":43,"area":null},{"id":"2202.13852","title":"Hyperbolic Graph Neural Networks: A Review of Methods and Applications","created":"2022-02-28","cited_by_count":40,"area":null},{"id":"2310.17110","title":"LLM4DyG: Can Large Language Models Solve Spatial-Temporal Problems on Dynamic Graphs?","created":"2023-10-26","cited_by_count":40,"area":null},{"id":"2204.08176","title":"HRCF: Enhancing Collaborative Filtering via Hyperbolic Geometric Regularization","created":"2022-04-18","cited_by_count":34,"area":null},{"id":"2202.10688","title":"Graph Lifelong Learning: A Survey","created":"2022-02-22","cited_by_count":30,"area":null},{"id":"2405.00476","title":"A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challenges","created":"2024-05-01","cited_by_count":30,"area":null},{"id":"2410.04010","title":"Hyperbolic Fine-Tuning for Large Language Models","created":"2024-10-05","cited_by_count":27,"area":null},{"id":"2311.11114","title":"Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization","created":"2023-11-18","cited_by_count":26,"area":null},{"id":"2207.09051","title":"HICF: Hyperbolic Informative Collaborative Filtering","created":"2022-07-19","cited_by_count":24,"area":null},{"id":"2208.14073","title":"A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning","created":"2022-08-30","cited_by_count":24,"area":null},{"id":"2210.10592","title":"DyTed: Disentangled Representation Learning for Discrete-time Dynamic Graph","created":"2022-10-19","cited_by_count":21,"area":null},{"id":"2306.09118","title":"Hyperbolic Representation Learning: Revisiting and Advancing","created":"2023-06-15","cited_by_count":21,"area":null},{"id":"2201.08554","title":"Enhancing Hyperbolic Graph Embeddings via Contrastive Learning","created":"2022-01-21","cited_by_count":20,"area":null},{"id":"2403.05026","title":"Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts","created":"2024-03-08","cited_by_count":19,"area":null},{"id":"2412.14695","title":"Lorentzian Residual Neural Networks","created":"2024-12-19","cited_by_count":17,"area":null},{"id":"2305.03555","title":"Contrastive Graph Clustering in Curvature Spaces","created":"2023-05-05","cited_by_count":15,"area":null},{"id":"2212.01793","title":"kHGCN: Tree-likeness Modeling via Continuous and Discrete Curvature Learning","created":"2022-12-04","cited_by_count":14,"area":null},{"id":"2304.07302","title":"HGWaveNet: A Hyperbolic Graph Neural Network for Temporal Link Prediction","created":"2023-04-14","cited_by_count":14,"area":null},{"id":"2202.06281","title":"Graph-adaptive Rectified Linear Unit for Graph Neural Networks","created":"2022-02-13","cited_by_count":12,"area":null},{"id":"2406.11389","title":"SEFraud: Graph-based Self-Explainable Fraud Detection via Interpretative Mask Learning","created":"2024-06-17","cited_by_count":12,"area":null},{"id":"2504.08912","title":"HyperCore: The Core Framework for Building Hyperbolic Foundation Models with Comprehensive Modules","created":"2025-04-11","cited_by_count":12,"area":null},{"id":"2407.12269","title":"UTG: Towards a Unified View of Snapshot and Event Based Models for Temporal Graphs","created":"2024-07-17","cited_by_count":10,"area":null},{"id":"2211.01214","title":"Time-aware Random Walk Diffusion to Improve Dynamic Graph Learning","created":"2022-11-02","cited_by_count":7,"area":null},{"id":"2204.07703","title":"TeleGraph: A Benchmark Dataset for Hierarchical Link Prediction","created":"2022-04-16","cited_by_count":6,"area":null},{"id":"2204.09508","title":"BSAL: A Framework of Bi-component Structure and Attribute Learning for Link Prediction","created":"2022-04-18","cited_by_count":6,"area":null},{"id":"2409.17986","title":"Supra-Laplacian Encoding for Transformer on Dynamic Graphs","created":"2024-09-26","cited_by_count":6,"area":null},{"id":"2110.06088","title":"ConTIG: Continuous Representation Learning on Temporal Interaction Graphs","created":"2021-09-27","cited_by_count":5,"area":null},{"id":"2204.12704","title":"Discovering Representative Attribute-stars via Minimum Description Length","created":"2022-04-27","cited_by_count":5,"area":null},{"id":"2311.15545","title":"Out-of-Distribution Generalized Dynamic Graph Neural Network for Human Albumin Prediction","created":"2023-11-27","cited_by_count":5,"area":null},{"id":"2305.03883","title":"SINCERE: Sequential Interaction Networks representation learning on Co-Evolving RiEmannian manifolds","created":"2023-05-06","cited_by_count":4,"area":null},{"id":"2312.13068","title":"Continuous-time Graph Representation with Sequential Survival Process","created":"2023-12-20","cited_by_count":4,"area":null},{"id":"2304.06970","title":"$\\text{H}^2\\text{TNE}$: Temporal Heterogeneous Information Network Embedding in Hyperbolic Spaces","created":"2023-04-14","cited_by_count":3,"area":null},{"id":"2401.01243","title":"Contrastive Sequential Interaction Network Learning on Co-Evolving Riemannian Spaces","created":"2024-01-02","cited_by_count":3,"area":null},{"id":"2405.06975","title":"Input Snapshots Fusion for Scalable Discrete-Time Dynamic Graph Neural Networks","created":"2024-05-11","cited_by_count":3,"area":null},{"id":"2407.18523","title":"DTFormer: A Transformer-Based Method for Discrete-Time Dynamic Graph Representation Learning","created":"2024-07-26","cited_by_count":3,"area":null},{"id":"2502.16431","title":"UniDyG: A Unified and Effective Representation Learning Approach for Large Dynamic Graphs","created":"2025-02-23","cited_by_count":3,"area":null},{"id":"2506.01596","title":"Understanding and Improving Laplacian Positional Encodings For Temporal GNNs","created":"2025-06-02","cited_by_count":3,"area":null},{"id":"2506.05393","title":"Are Large Language Models Good Temporal Graph Learners?","created":"2025-06-03","cited_by_count":3,"area":null},{"id":"2211.04050","title":"Hyperbolic Graph Representation Learning: A Tutorial","created":"2022-11-08","cited_by_count":2,"area":null},{"id":"2406.10426","title":"MiNT: Multi-Network Training for Transfer Learning on Temporal Graphs","created":"2024-06-14","cited_by_count":2,"area":null},{"id":"2412.20256","title":"Towards Ideal Temporal Graph Neural Networks: Evaluations and Conclusions after 10,000 GPU Hours","created":"2024-12-28","cited_by_count":2,"area":null},{"id":"2501.16002","title":"ScaDyG:A New Paradigm for Large-scale Dynamic Graph Learning","created":"2025-01-27","cited_by_count":2,"area":null},{"id":"2505.14417","title":"Towards Non-Euclidean Foundation Models: Advancing AI Beyond Euclidean Frameworks","created":"2025-05-20","cited_by_count":1,"area":null},{"id":"2601.14115","title":"Riemannian Liquid Spatio-Temporal Graph Network","created":"2026-01-20","cited_by_count":1,"area":null},{"id":"2603.01626","title":"Towards OOD Generalization in Dynamic Graphs via Causal Invariant Learning","created":"2026-03-02","cited_by_count":1,"area":null},{"id":"2603.17112","title":"Cascade-Aware Multi-Agent Routing: Spatio-Temporal Sidecars and Geometry-Switching","created":"2026-03-17","cited_by_count":1,"area":null},{"id":"2606.22914","title":"PromptDyG: Test-Time Prompt Adaptation on Dynamic Graphs","created":"2026-06-22","cited_by_count":1,"area":null},{"id":"2210.12690","title":"DyCSC: Modeling the Evolutionary Process of Dynamic Networks Based on Cluster Structure","created":"2022-10-23","cited_by_count":0,"area":null},{"id":"2312.00485","title":"Backbone-based Dynamic Graph Spatio-Temporal Network for Epidemic Forecasting","created":"2023-12-01","cited_by_count":0,"area":null},{"id":"2406.10367","title":"Disentangled Hyperbolic Representation Learning for Heterogeneous Graphs","created":"2024-06-14","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":"2409.09262","title":"Informative Subgraphs Aware Masked Auto-Encoder in Dynamic Graphs","created":"2024-09-14","cited_by_count":0,"area":null},{"id":"2502.11345","title":"Hierarchical Graph Topic Modeling with Topic Tree-based Transformer","created":"2025-02-17","cited_by_count":0,"area":null},{"id":"2504.05144","title":"Online Cluster-Based Parameter Control for Metaheuristic","created":"2025-04-07","cited_by_count":0,"area":null},{"id":"2504.11367","title":"Network Alignment","created":"2025-04-15","cited_by_count":0,"area":null},{"id":"2505.09017","title":"DyGSSM: Multi-view Dynamic Graph Embeddings with State Space Model Gradient Update","created":"2025-05-13","cited_by_count":0,"area":null},{"id":"2510.05569","title":"Efficient Learning-based Graph Simulation for Temporal Graphs","created":"2025-10-07","cited_by_count":0,"area":null},{"id":"2512.18826","title":"Hyperbolic Graph Embeddings: a Survey and an Evaluation on Anomaly Detection","created":"2025-12-21","cited_by_count":0,"area":null},{"id":"2605.12759","title":"Predicting Channel Closures in the Lightning Network with Machine Learning","created":"2026-05-12","cited_by_count":0,"area":null},{"id":"2605.28155","title":"Temporal Hyperbolic Graph Representation Learning for Scale-Free Internet Routing and Delay Prediction","created":"2026-05-27","cited_by_count":0,"area":null},{"id":"2609.08286","title":"HypLTSF: A Hyperbolic Geometric View of Multi-Scale Hierarchies for Long-Term Time Series Forecasting","created":"2026-09-08","cited_by_count":0,"area":null}],"next_cursor":null}