{"total":131,"rows":[{"id":"2303.13047","title":"Towards Better Dynamic Graph Learning: New Architecture and Unified Library","created":"2023-03-23","cited_by_count":157,"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":"2502.06784","title":"RelGNN: Composite Message Passing for Relational Deep Learning","created":"2025-02-10","cited_by_count":48,"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":"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":"2410.02195","title":"BACKTIME: Backdoor Attacks on Multivariate Time Series Forecasting","created":"2024-10-03","cited_by_count":18,"area":null},{"id":"2304.05729","title":"Dynamic Graph Representation Learning with Neural Networks: A Survey","created":"2023-04-12","cited_by_count":15,"area":null},{"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":"2402.11933","title":"SLADE: Detecting Dynamic Anomalies in Edge Streams without Labels via Self-Supervised Learning","created":"2024-02-19","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":"2402.15113","title":"MSPipe: Efficient Temporal GNN Training via Staleness-Aware Pipeline","created":"2024-02-23","cited_by_count":12,"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":"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":"2308.10779","title":"Spear and Shield: Adversarial Attacks and Defense Methods for Model-Based Link Prediction on Continuous-Time Dynamic Graphs","created":"2023-08-21","cited_by_count":9,"area":null},{"id":"2406.13214","title":"Self-Explainable Temporal Graph Networks based on Graph Information Bottleneck","created":"2024-06-19","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":"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":"2306.11147","title":"CAT-Walk: Inductive Hypergraph Learning via Set Walks","created":"2023-06-19","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":"2412.16435","title":"THeGCN: Temporal Heterophilic Graph Convolutional Network","created":"2024-12-21","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":"2309.15730","title":"Temporal graph models fail to capture global temporal dynamics","created":"2023-09-27","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":"2309.02012","title":"iLoRE: Dynamic Graph Representation with Instant Long-term Modeling and Re-occurrence Preservation","created":"2023-09-05","cited_by_count":4,"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":"2306.14340","title":"GPatcher: A Simple and Adaptive MLP Model for Alleviating Graph Heterophily","created":"2023-06-25","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":"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":"2506.12764","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":"2507.02151","title":"Non-exchangeable Conformal Prediction for Temporal Graph Neural Networks","created":"2025-07-02","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":"2402.05396","title":"TASER: Temporal Adaptive Sampling for Fast and Accurate Dynamic Graph Representation Learning","created":"2024-02-08","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":"2407.18772","title":"Learning production functions for supply chains with graph neural networks","created":"2024-07-26","cited_by_count":2,"area":null},{"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":"2507.03267","title":"GDGB: A Benchmark for Generative Dynamic Text-Attributed Graph Learning","created":"2025-07-04","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":"2510.18467","title":"Simple and Efficient Heterogeneous Temporal Graph Neural Network","created":"2025-10-21","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":"2512.11352","title":"CAT: Can Trust be Predicted with Context-Awareness in Dynamic Heterogeneous Networks?","created":"2025-12-12","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":"2405.17182","title":"Exploring the Performance of Continuous-Time Dynamic Link Prediction Algorithms","created":"2024-05-27","cited_by_count":1,"area":null},{"id":"2412.00984","title":"TGTOD: A Global Temporal Graph Transformer for Outlier Detection at Scale","created":"2024-12-01","cited_by_count":1,"area":null},{"id":"2412.03783","title":"Expressivity of Representation Learning on Continuous-Time Dynamic Graphs: An Information-Flow Centric Review","created":"2024-12-05","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":"2505.15746","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":"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":"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":"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":"2308.07883","title":"Towards Temporal Edge Regression: A Case Study on Agriculture Trade Between Nations","created":"2023-08-15","cited_by_count":0,"area":null},{"id":"2310.00183","title":"On the Equivalence of Graph Convolution and Mixup","created":"2023-09-29","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":"2403.13338","title":"Adaptive Critical Subgraph Mining for Cognitive Impairment Conversion Prediction with T1-MRI-based Brain Network","created":"2024-03-20","cited_by_count":0,"area":null},{"id":"2405.19062","title":"Invariant Graph Representations for Continuous-Time Dynamic Graphs Under Distribution Shifts","created":"2024-05-29","cited_by_count":0,"area":null},{"id":"2407.14270","title":"T- Hop: A framework for studying the importance path information in molecular graphs for chemical property prediction","created":"2024-06-29","cited_by_count":0,"area":null},{"id":"2408.02704","title":"Spatial-temporal Graph Convolutional Networks with Diversified Transformation for Dynamic Graph Representation Learning","created":"2024-08-05","cited_by_count":0,"area":null},{"id":"2408.07087","title":"A Novel Spatiotemporal Coupling Graph Convolutional Network","created":"2024-08-09","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":"2408.12753","title":"Contrastive Representation Learning for Dynamic Link Prediction in Temporal Networks","created":"2024-08-22","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":"2503.00852","title":"A Transfer Framework for Enhancing Temporal Graph Learning in Data-Scarce Settings","created":"2025-03-02","cited_by_count":0,"area":null},{"id":"2504.07337","title":"FLASH: Flexible Learning of Adaptive Sampling from History in Temporal Graph Neural Networks","created":"2025-04-09","cited_by_count":0,"area":null},{"id":"2504.11386","title":"Trajectory Encoding Temporal Graph Networks","created":"2025-04-15","cited_by_count":0,"area":null},{"id":"2504.17641","title":"PTCL: Pseudo-Label Temporal Curriculum Learning for Label-Limited Dynamic Graph","created":"2025-04-24","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":"2505.12526","title":"Never Skip a Batch: Dense Learning of Temporal GNNs via Adaptive Pseudo-Supervision","created":"2025-05-18","cited_by_count":0,"area":null},{"id":"2508.14859","title":"Graph Structure Learning with Temporal Graph Information Bottleneck for Inductive Representation Learning","created":"2025-08-20","cited_by_count":0,"area":null},{"id":"2508.17236","title":"Learning Short-Term and Long-Term Patterns of High-Order Dynamics in Real-World Networks","created":"2025-08-24","cited_by_count":0,"area":null}],"next_cursor":"100"}