{"total":53,"rows":[{"id":"2307.01504","title":"All in One: Multi-task Prompting for Graph Neural Networks","created":"2023-07-04","cited_by_count":148,"area":null},{"id":"2208.07239","title":"ROLAND: Graph Learning Framework for Dynamic Graphs","created":"2022-08-15","cited_by_count":132,"area":null},{"id":"2104.07788","title":"PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models","created":"2021-04-15","cited_by_count":118,"area":null},{"id":"2408.14512","title":"LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings","created":"2024-08-25","cited_by_count":61,"area":null},{"id":"2110.15174","title":"On Provable Benefits of Depth in Training Graph Convolutional Networks","created":"2021-10-28","cited_by_count":58,"area":null},{"id":"2306.09368","title":"Warpformer: A Multi-scale Modeling Approach for Irregular Clinical Time Series","created":"2023-06-14","cited_by_count":55,"area":null},{"id":"2102.07736","title":"Network of Tensor Time Series","created":"2021-02-15","cited_by_count":32,"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":"2404.18211","title":"A survey of dynamic graph neural networks","created":"2024-04-28","cited_by_count":24,"area":null},{"id":"2307.06104","title":"Deep learning for dynamic graphs: models and benchmarks","created":"2023-07-12","cited_by_count":19,"area":null},{"id":"2110.15528","title":"Deconvolutional Networks on Graph Data","created":"2021-10-29","cited_by_count":16,"area":null},{"id":"2203.01093","title":"Information Gain Propagation: a new way to Graph Active Learning with Soft Labels","created":"2022-03-02","cited_by_count":16,"area":null},{"id":"2203.16110","title":"Weakly-supervised Temporal Path Representation Learning with Contrastive Curriculum Learning -- Extended Version","created":"2022-03-30","cited_by_count":15,"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":"2211.15335","title":"You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets","created":"2022-11-28","cited_by_count":14,"area":null},{"id":"2303.10909","title":"Graph Neural Rough Differential Equations for Traffic Forecasting","created":"2023-03-20","cited_by_count":14,"area":null},{"id":"2309.12028","title":"Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting","created":"2023-09-21","cited_by_count":14,"area":null},{"id":"2302.13083","title":"Knowledge Graph Completion with Counterfactual Augmentation","created":"2023-02-25","cited_by_count":13,"area":null},{"id":"2404.19508","title":"Temporal Graph ODEs for Irregularly-Sampled Time Series","created":"2024-04-30","cited_by_count":11,"area":null},{"id":"2103.07016","title":"On the Equivalence Between Temporal and Static Graph Representations for Observational Predictions","created":"2021-03-12","cited_by_count":10,"area":null},{"id":"2402.03785","title":"Weakly Supervised Anomaly Detection via Knowledge-Data Alignment","created":"2024-02-06","cited_by_count":10,"area":null},{"id":"2505.18728","title":"Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling","created":"2025-05-24","cited_by_count":10,"area":null},{"id":"2308.12551","title":"A Co-training Approach for Noisy Time Series Learning","created":"2023-08-24","cited_by_count":8,"area":null},{"id":"2110.06421","title":"Revisiting Latent-Space Interpolation via a Quantitative Evaluation Framework","created":"2021-10-13","cited_by_count":7,"area":null},{"id":"2209.11414","title":"Enabling Homogeneous GNNs to Handle Heterogeneous Graphs via Relation Embedding","created":"2022-09-23","cited_by_count":7,"area":null},{"id":"2408.12185","title":"Rank and Align: Towards Effective Source-free Graph Domain Adaptation","created":"2024-08-22","cited_by_count":7,"area":null},{"id":"2211.08378","title":"Anomaly Detection in Multiplex Dynamic Networks: from Blockchain Security to Brain Disease Prediction","created":"2022-11-15","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":"2309.12694","title":"Recurrent Temporal Revision Graph Networks","created":"2023-09-22","cited_by_count":5,"area":null},{"id":"2405.02299","title":"Deep Reinforcement Learning for Modelling Protein Complexes","created":"2024-03-11","cited_by_count":5,"area":null},{"id":"2210.13149","title":"Binary Graph Convolutional Network with Capacity Exploration","created":"2022-10-24","cited_by_count":4,"area":null},{"id":"2303.08964","title":"CS-TGN: Community Search via Temporal Graph Neural Networks","created":"2023-03-15","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":"2211.06605","title":"Comprehensive Analysis of Over-smoothing in Graph Neural Networks from Markov Chains Perspective","created":"2022-11-12","cited_by_count":3,"area":null},{"id":"2304.06831","title":"DGNN-Booster: A Generic FPGA Accelerator Framework For Dynamic Graph Neural Network Inference","created":"2023-04-13","cited_by_count":3,"area":null},{"id":"2406.12065","title":"STNAGNN: Data-driven Spatio-temporal Brain Connectivity beyond FC","created":"2024-06-17","cited_by_count":3,"area":null},{"id":"2108.08754","title":"Temporal Graph Network Embedding with Causal Anonymous Walks Representations","created":"2021-08-19","cited_by_count":2,"area":null},{"id":"2111.07032","title":"Learning to Evolve on Dynamic Graphs","created":"2021-11-13","cited_by_count":2,"area":null},{"id":"2311.01647","title":"Calibrate and Boost Logical Expressiveness of GNN Over Multi-Relational and Temporal Graphs","created":"2023-11-03","cited_by_count":2,"area":null},{"id":"2505.11930","title":"The Logical Expressiveness of Temporal GNNs via Two-Dimensional Product Logics","created":"2025-05-17","cited_by_count":2,"area":null},{"id":"2105.10478","title":"Spatial-temporal Conv-sequence Learning with Accident Encoding for Traffic Flow Prediction","created":"2021-05-21","cited_by_count":1,"area":null},{"id":"2205.15127","title":"Universal Deep GNNs: Rethinking Residual Connection in GNNs from a Path Decomposition Perspective for Preventing the Over-smoothing","created":"2022-05-30","cited_by_count":1,"area":null},{"id":"2401.00713","title":"Graph Neural Networks in Intelligent Transportation Systems: Advances, Applications and Trends","created":"2024-01-01","cited_by_count":1,"area":null},{"id":"2510.00457","title":"UrbanGraph: Physics-Informed Spatio-Temporal Dynamic Heterogeneous Graphs for Urban Microclimate Prediction","created":"2025-10-01","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":"2107.01528","title":"Incorporating Reachability Knowledge into a Multi-Spatial Graph Convolution Based Seq2Seq Model for Traffic Forecasting","created":"2021-07-04","cited_by_count":0,"area":null},{"id":"2110.10849","title":"Using NASA Satellite Data Sources and Geometric Deep Learning to Uncover Hidden Patterns in COVID-19 Clinical Severity","created":"2021-10-21","cited_by_count":0,"area":null},{"id":"2203.16256","title":"Research topic trend prediction of scientific papers based on spatial enhancement and dynamic graph convolution network","created":"2022-03-30","cited_by_count":0,"area":null},{"id":"2204.08476","title":"Research on Domain Information Mining and Theme Evolution of Scientific Papers","created":"2022-04-18","cited_by_count":0,"area":null},{"id":"2210.08847","title":"tegdet: An extensible Python Library for Anomaly Detection using Time-Evolving Graphs","created":"2022-10-17","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":"2408.07624","title":"Battery GraphNets : Relational Learning for Lithium-ion Batteries(LiBs) Life Estimation","created":"2024-08-14","cited_by_count":0,"area":null},{"id":"2606.24011","title":"Low-rank Updates in Slowly Time-varying Graphs for Spatial-Temporal Signal Interpolation","created":"2026-06-22","cited_by_count":0,"area":null}],"next_cursor":null}