{"total":157,"rows":[{"id":"2307.01026","title":"Temporal Graph Benchmark for Machine Learning on Temporal Graphs","created":"2023-07-03","cited_by_count":138,"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":"2406.02740","title":"Long Range Propagation on Continuous-Time Dynamic Graphs","created":"2024-06-04","cited_by_count":28,"area":null},{"id":"2310.19324","title":"TempME: Towards the Explainability of Temporal Graph Neural Networks via Motif Discovery","created":"2023-10-30","cited_by_count":27,"area":null},{"id":"2406.12072","title":"DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphs","created":"2024-06-17","cited_by_count":27,"area":null},{"id":"2506.16654","title":"Relational Deep Learning: Challenges, Foundations and Next-Generation Architectures","created":"2025-06-19","cited_by_count":23,"area":null},{"id":"2410.04013","title":"Improving Temporal Link Prediction via Temporal Walk Matrix Projection","created":"2024-10-05","cited_by_count":21,"area":null},{"id":"2406.07926","title":"Efficient Neural Common Neighbor for Temporal Graph Link Prediction","created":"2024-06-12","cited_by_count":18,"area":null},{"id":"2408.06966","title":"DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs","created":"2024-08-13","cited_by_count":17,"area":"ssm"},{"id":"2405.14407","title":"Dynamic Graph Unlearning: A General and Efficient Post-Processing Method via Gradient Transformation","created":"2024-05-23","cited_by_count":15,"area":"safety"},{"id":"2502.02975","title":"TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics","created":"2025-02-05","cited_by_count":15,"area":null},{"id":"2408.04713","title":"DyGMamba: Efficiently Modeling Long-Term Temporal Dependency on Continuous-Time Dynamic Graphs with State Space Models","created":"2024-08-08","cited_by_count":14,"area":null},{"id":"2411.01410","title":"PageRank Bandits for Link Prediction","created":"2024-11-03","cited_by_count":14,"area":null},{"id":"2405.13937","title":"Node-Time Conditional Prompt Learning In Dynamic Graphs","created":"2024-05-22","cited_by_count":11,"area":null},{"id":"2405.17473","title":"Repeat-Aware Neighbor Sampling for Dynamic Graph Learning","created":"2024-05-24","cited_by_count":11,"area":null},{"id":"2311.18526","title":"HOT: Higher-Order Dynamic Graph Representation Learning with Efficient Transformers","created":"2023-11-30","cited_by_count":10,"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":"2407.20871","title":"Co-Neighbor Encoding Schema: A Light-cost Structure Encoding Method for Dynamic Link Prediction","created":"2024-07-30","cited_by_count":10,"area":null},{"id":"2308.16385","title":"BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks","created":"2023-08-31","cited_by_count":9,"area":null},{"id":"2307.12510","title":"An Empirical Evaluation of Temporal Graph Benchmark","created":"2023-07-24","cited_by_count":8,"area":null},{"id":"2406.11891","title":"Towards Adaptive Neighborhood for Advancing Temporal Interaction Graph Modeling","created":"2024-06-14","cited_by_count":8,"area":null},{"id":"2502.10914","title":"LLM-driven Knowledge Distillation for Dynamic Text-Attributed Graphs","created":"2025-02-15","cited_by_count":8,"area":"distill"},{"id":"2305.00664","title":"EvoluNet: Advancing Dynamic Non-IID Transfer Learning on Graphs","created":"2023-05-01","cited_by_count":7,"area":null},{"id":"2402.02168","title":"Enhancing Cross-domain Link Prediction via Evolution Process Modeling","created":"2024-02-03","cited_by_count":7,"area":null},{"id":"2306.07699","title":"Time-aware Graph Structure Learning via Sequence Prediction on Temporal Graphs","created":"2023-06-13","cited_by_count":6,"area":null},{"id":"2308.09780","title":"Event-based Dynamic Graph Representation Learning for Patent Application Trend Prediction","created":"2023-08-04","cited_by_count":6,"area":null},{"id":"2407.16959","title":"Dynamic Graph Transformer with Correlated Spatial-Temporal Positional Encoding","created":"2024-07-24","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":"2411.03596","title":"Enhancing the Expressivity of Temporal Graph Networks through Source-Target Identification","created":"2024-11-06","cited_by_count":6,"area":null},{"id":"2502.21185","title":"A Survey of Link Prediction in Temporal Networks","created":"2025-02-28","cited_by_count":6,"area":null},{"id":"2503.14411","title":"Unifying Text Semantics and Graph Structures for Temporal Text-attributed Graphs with Large Language Models","created":"2025-03-18","cited_by_count":6,"area":null},{"id":"2412.07273","title":"Temporal-Aware Evaluation and Learning for Temporal Graph Neural Networks","created":"2024-12-10","cited_by_count":5,"area":null},{"id":"2505.19408","title":"Future Link Prediction Without Memory or Aggregation","created":"2025-05-26","cited_by_count":5,"area":null},{"id":"2506.00431","title":"TIDFormer: Exploiting Temporal and Interactive Dynamics Makes A Great Dynamic Graph Transformer","created":"2025-05-31","cited_by_count":5,"area":null},{"id":"2405.18050","title":"Learning-Based Link Anomaly Detection in Continuous-Time Dynamic Graphs","created":"2024-05-28","cited_by_count":4,"area":null},{"id":"2406.02362","title":"Temporal Graph Rewiring with Expander Graphs","created":"2024-06-04","cited_by_count":4,"area":null},{"id":"2505.04461","title":"A Survey on Temporal Interaction Graph Representation Learning: Progress, Challenges, and Opportunities","created":"2025-05-07","cited_by_count":4,"area":null},{"id":"2510.09416","title":"What Do Temporal Graph Learning Models Learn?","created":"2025-10-10","cited_by_count":4,"area":null},{"id":"2608.18104","title":"Self-Evolving Agents as Dynamic Graph Transformation: A Survey and New Perspective","created":"2026-06-10","cited_by_count":4,"area":"agents"},{"id":"2406.04897","title":"From Link Prediction to Forecasting: Addressing Challenges in Batch-based Temporal Graph Learning","created":"2024-06-07","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":"2502.19651","title":"Unlocking Multi-Modal Potentials for Link Prediction on Dynamic Text-Attributed Graphs","created":"2025-02-27","cited_by_count":3,"area":null},{"id":"2503.03258","title":"Exploring the Potential of Large Language Models as Predictors in Dynamic Text-Attributed Graphs","created":"2025-03-05","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":"2506.08309","title":"Learnable Spatial-Temporal Positional Encoding for Link Prediction","created":"2025-06-10","cited_by_count":3,"area":null},{"id":"2506.12764","title":"Base3: a simple interpolation-based ensemble method for robust dynamic link prediction","created":"2025-06-15","cited_by_count":3,"area":null},{"id":"2506.20324","title":"Permutation Equivariant Neural Controlled Differential Equations for Dynamic Graph Representation Learning","created":"2025-06-25","cited_by_count":3,"area":null},{"id":"2510.07586","title":"TGM: a Modular and Efficient Library for Machine Learning on Temporal Graphs","created":"2025-10-08","cited_by_count":3,"area":null},{"id":"2601.22454","title":"Temporal Graph Pattern Machine","created":"2026-01-30","cited_by_count":3,"area":null},{"id":"2310.02721","title":"Leveraging Temporal Graph Networks Using Module Decoupling","created":"2023-10-04","cited_by_count":2,"area":null},{"id":"2402.01964","title":"Scalable and Efficient Temporal Graph Representation Learning via Forward Recent Sampling","created":"2024-02-03","cited_by_count":2,"area":null},{"id":"2402.05944","title":"Todyformer: Towards Holistic Dynamic Graph Transformers with Structure-Aware Tokenization","created":"2024-02-02","cited_by_count":2,"area":null},{"id":"2403.16846","title":"CoDy: Counterfactual Explainers for Dynamic Graphs","created":"2024-03-25","cited_by_count":2,"area":null},{"id":"2407.08500","title":"Latent Conditional Diffusion-based Data Augmentation for Continuous-Time Dynamic Graph Model","created":"2024-07-11","cited_by_count":2,"area":"diffusion"},{"id":"2411.11259","title":"Graph Retention Networks for Dynamic Graphs","created":"2024-11-18","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":"2504.08129","title":"Between Linear and Sinusoidal: Rethinking the Time Encoder in Dynamic Graph Learning","created":"2025-04-10","cited_by_count":2,"area":null},{"id":"2506.12588","title":"Are We Really Measuring Progress? Transferring Insights from Evaluating Recommender Systems to Temporal Link Prediction","created":"2025-06-14","cited_by_count":2,"area":null},{"id":"2507.03267","title":"GDGB: A Benchmark for Generative Dynamic Text-Attributed Graph Learning","created":"2025-07-04","cited_by_count":2,"area":null},{"id":"2507.10183","title":"T-GRAB: A Synthetic Diagnostic Benchmark for Learning on Temporal Graphs","created":"2025-07-14","cited_by_count":2,"area":null},{"id":"2510.06940","title":"Revisiting Node Affinity Prediction in Temporal Graphs","created":"2025-10-08","cited_by_count":2,"area":null},{"id":"2510.23577","title":"TAMI: Taming Heterogeneity in Temporal Interactions for Temporal Graph Link Prediction","created":"2025-10-23","cited_by_count":2,"area":null},{"id":"2511.18631","title":"FOS: A Large-Scale Temporal Graph Benchmark for Scientific Interdisciplinary Link Prediction","created":"2025-11-23","cited_by_count":2,"area":null},{"id":"2601.11954","title":"Data-centric Prompt Tuning for Dynamic Graphs","created":"2026-01-17","cited_by_count":2,"area":null},{"id":"2608.06031","title":"Dynamic Graph Prompting via Topology-Routed Mixed-Curvature Experts","created":"2026-08-06","cited_by_count":2,"area":null},{"id":"2310.09787","title":"Dynamic Link Prediction for New Nodes in Temporal Graph Networks","created":"2023-10-15","cited_by_count":1,"area":null},{"id":"2402.03651","title":"Temporal Graph Analysis with TGX","created":"2024-02-06","cited_by_count":1,"area":null},{"id":"2405.17182","title":"Exploring the Performance of Continuous-Time Dynamic Link Prediction Algorithms","created":"2024-05-27","cited_by_count":1,"area":null},{"id":"2407.17070","title":"Curriculum Negative Mining For Temporal Networks","created":"2024-07-24","cited_by_count":1,"area":null},{"id":"2411.01070","title":"Explainable Spatio-Temporal GCNNs for Irregular Multivariate Time Series: Architecture and Application to ICU Patient Data","created":"2024-11-01","cited_by_count":1,"area":null},{"id":"2411.15893","title":"Distribution-aware Online Continual Learning for Urban Spatio-Temporal Forecasting","created":"2024-11-24","cited_by_count":1,"area":null},{"id":"2412.21046","title":"Mind the truncation gap: challenges of learning on dynamic graphs with recurrent architectures","created":"2024-12-30","cited_by_count":1,"area":null},{"id":"2503.01580","title":"A Selective Learning Method for Temporal Graph Continual Learning","created":"2025-03-03","cited_by_count":1,"area":null},{"id":"2504.00328","title":"Simple yet Effective Node Property Prediction on Edge Streams under Distribution Shifts","created":"2025-04-01","cited_by_count":1,"area":null},{"id":"2505.00887","title":"Rethinking Time Encoding via Learnable Transformation Functions","created":"2025-05-01","cited_by_count":1,"area":null},{"id":"2505.15746","title":"Higher-order Structure Boosts Link Prediction on Temporal Graphs","created":"2025-05-21","cited_by_count":1,"area":null},{"id":"2507.11836","title":"HyperEvent: A Strong Baseline for Dynamic Link Prediction via Relative Structural Encoding","created":"2025-07-16","cited_by_count":1,"area":null},{"id":"2509.00975","title":"Self-Exploring Language Models for Explainable Link Forecasting on Temporal Graphs via Reinforcement Learning","created":"2025-08-31","cited_by_count":1,"area":null},{"id":"2510.11903","title":"Integrating Sequential and Relational Modeling for User Events: Datasets and Prediction Tasks","created":"2025-10-13","cited_by_count":1,"area":null},{"id":"2510.13789","title":"T3former: Temporal Graph Classification with Topological Machine Learning","created":"2025-10-15","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":"2601.23233","title":"Sequence Diffusion Model for Temporal Link Prediction in Continuous-Time Dynamic Graph","created":"2026-01-30","cited_by_count":1,"area":"diffusion"},{"id":"2602.01124","title":"ChronoSpike: An Adaptive Spiking Graph Neural Network for Dynamic Graphs","created":"2026-02-01","cited_by_count":1,"area":null},{"id":"2603.13360","title":"Graph2Video: Leveraging Video Models to Model Dynamic Graph Evolution","created":"2026-03-09","cited_by_count":1,"area":null},{"id":"2605.13540","title":"Decoupled and Divergence-Conditioned Prompt for Multi-domain Dynamic Graph Foundation Models","created":"2026-05-13","cited_by_count":1,"area":null},{"id":"2605.29453","title":"Forget Less, Generalize More: Unifying Temporal and Structural Adaptation for Dynamic Graphs","created":"2026-05-28","cited_by_count":1,"area":null},{"id":"2605.29526","title":"Temporal Motif-aware Graph Test-time Adaptation for OOD Blockchain Anomaly Detection","created":"2026-05-28","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":"2608.06765","title":"Where Does Neural Advantage Arise in Continuous-Time Dynamic Graph Prediction?","created":"2026-08-07","cited_by_count":1,"area":null},{"id":"2609.38927","title":"World-as-Graph: Relational World Modeling Through Latent Space Graphs","created":"2026-09-30","cited_by_count":1,"area":"wm"},{"id":"2304.10079","title":"Dynamic Graph Representation Learning via Edge Temporal States Modeling and Structure-reinforced Transformer","created":"2023-04-20","cited_by_count":0,"area":null},{"id":"2311.16605","title":"LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning","created":"2023-11-28","cited_by_count":0,"area":null},{"id":"2312.07983","title":"Multi-perspective Feedback-attention Coupling Model for Continuous-time Dynamic Graphs","created":"2023-12-13","cited_by_count":0,"area":null},{"id":"2408.06039","title":"Spacetime $E(n)$-Transformer: Equivariant Attention for Spatio-temporal Graphs","created":"2024-08-12","cited_by_count":0,"area":null},{"id":"2408.09092","title":"Dynamic Graph Representation Learning for Passenger Behavior Prediction","created":"2024-08-17","cited_by_count":0,"area":null},{"id":"2410.01367","title":"Towards Dynamic Graph Neural Networks with Provably High-Order Expressive Power","created":"2024-10-02","cited_by_count":0,"area":null},{"id":"2410.10464","title":"Information propagation dynamics in Deep Graph Networks","created":"2024-10-14","cited_by_count":0,"area":null},{"id":"2501.11880","title":"Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic Graphs","created":"2025-01-21","cited_by_count":0,"area":null}],"next_cursor":"100"}