{"total":63,"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":"2302.11636","title":"Do We Really Need Complicated Model Architectures For Temporal Networks?","created":"2023-02-22","cited_by_count":143,"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":"2207.05342","title":"Video Graph Transformer for Video Question Answering","created":"2022-07-12","cited_by_count":63,"area":null},{"id":"2302.13668","title":"Contrastive Video Question Answering via Video Graph Transformer","created":"2023-02-27","cited_by_count":32,"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":"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":"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":"2210.08189","title":"Parameter-free Dynamic Graph Embedding for Link Prediction","created":"2022-10-15","cited_by_count":12,"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":"2412.16447","title":"A Generalizable Anomaly Detection Method in Dynamic Graphs","created":"2024-12-21","cited_by_count":10,"area":null},{"id":"2307.12510","title":"An Empirical Evaluation of Temporal Graph Benchmark","created":"2023-07-24","cited_by_count":8,"area":null},{"id":"2302.11354","title":"Learning Dynamic Graph Embeddings with Neural Controlled Differential Equations","created":"2023-02-22","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":"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":"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":"2110.06088","title":"ConTIG: Continuous Representation Learning on Temporal Interaction Graphs","created":"2021-09-27","cited_by_count":5,"area":null},{"id":"2210.16906","title":"DyG2Vec: Efficient Representation Learning for Dynamic Graphs","created":"2022-10-30","cited_by_count":5,"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":"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":"2510.09416","title":"What Do Temporal Graph Learning Models Learn?","created":"2025-10-10","cited_by_count":4,"area":null},{"id":"2402.16387","title":"On the Generalization Capability of Temporal Graph Learning Algorithms: Theoretical Insights and a Simpler Method","created":"2024-02-26","cited_by_count":3,"area":null},{"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":"2506.08309","title":"Learnable Spatial-Temporal Positional Encoding for Link Prediction","created":"2025-06-10","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":"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":"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":"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":"2310.09787","title":"Dynamic Link Prediction for New Nodes in Temporal Graph Networks","created":"2023-10-15","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":"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":"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":"2107.01326","title":"SHORING: Design Provable Conditional High-Order Interaction Network via Symbolic Testing","created":"2021-07-03","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":"2408.09092","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":"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":"2601.22427","title":"CoDCL: Counterfactual-Inspired Augmentation Contrastive Learning for Temporal Link Prediction in Social Networks","created":"2026-01-30","cited_by_count":0,"area":null},{"id":"2602.20019","title":"Learning Discriminative and Generalizable Anomaly Detector for Dynamic Graph with Limited Supervision","created":"2026-02-23","cited_by_count":0,"area":null},{"id":"2602.23135","title":"An Analysis of Role Semantics in Directed Dynamic Graph Modeling for Edge Classification","created":"2026-02-26","cited_by_count":0,"area":null},{"id":"2605.06073","title":"PRISM: Iterative Cross-Modal Posterior Refinement for Dynamic Text-Attributed Graphs","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2605.16112","title":"Attention Dispersion in Dynamic Graph Transformers: Diagnosis and a Transferable Fix","created":"2026-05-15","cited_by_count":0,"area":null},{"id":"2605.24971","title":"TGFormer: Towards Temporal Graph Transformer with Auto-Correlation Mechanism","created":"2026-05-24","cited_by_count":0,"area":null},{"id":"2605.25548","title":"SiST-GNN: Simultaneous Spatial-Temporal Message Passing for Dynamic Graph Representation Learning","created":"2026-05-25","cited_by_count":0,"area":null},{"id":"2606.04672","title":"Learning Long Range Spatio-Temporal Representations over Continuous Time Dynamic Graphs with State Space Models","created":"2026-06-03","cited_by_count":0,"area":null},{"id":"2606.10071","title":"Temporal Sheaf Neural Networks with Dynamic Orthogonal Transport","created":"2026-06-08","cited_by_count":0,"area":null},{"id":"2609.08226","title":"TTGBench: Benchmarking Topological Evolution and Semantic Drift in Text-attributed Temporal Graphs","created":"2026-09-08","cited_by_count":0,"area":null},{"id":"2609.32849","title":"Transfer Learning for Edge Classification on Dynamic Text-Attributed Graphs","created":"2026-09-26","cited_by_count":0,"area":null}],"next_cursor":null}