{"total":84,"rows":[{"id":"2101.05974","title":"Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks","created":"2021-01-15","cited_by_count":193,"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":"2107.03767","title":"Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Space","created":"2021-07-08","cited_by_count":67,"area":null},{"id":"2204.01855","title":"A Survey on Graph Representation Learning Methods","created":"2022-04-04","cited_by_count":66,"area":null},{"id":"2302.01018","title":"Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities","created":"2023-02-02","cited_by_count":61,"area":null},{"id":"2101.01229","title":"A Survey on Embedding Dynamic Graphs","created":"2021-01-04","cited_by_count":60,"area":null},{"id":"2209.01084","title":"Neighborhood-aware Scalable Temporal Network Representation Learning","created":"2022-09-02","cited_by_count":54,"area":null},{"id":"2104.02228","title":"Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs","created":"2021-04-06","cited_by_count":50,"area":null},{"id":"2106.14880","title":"HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps","created":"2021-06-28","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":"2305.13825","title":"Continual Learning on Dynamic Graphs via Parameter Isolation","created":"2023-05-23","cited_by_count":36,"area":null},{"id":"2305.18612","title":"Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders","created":"2023-05-29","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":"2311.11114","title":"Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization","created":"2023-11-18","cited_by_count":26,"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":"2307.04287","title":"Generalizing Graph ODE for Learning Complex System Dynamics across Environments","created":"2023-07-10","cited_by_count":23,"area":null},{"id":"2403.07185","title":"Uncertainty in Graph Neural Networks: A Survey","created":"2024-03-11","cited_by_count":23,"area":null},{"id":"2102.13177","title":"Efficient and Interpretable Robot Manipulation with Graph Neural Networks","created":"2021-02-25","cited_by_count":22,"area":null},{"id":"2205.09802","title":"Label-invariant Augmentation for Semi-Supervised Graph Classification","created":"2022-05-19","cited_by_count":19,"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":"2304.05729","title":"Dynamic Graph Representation Learning with Neural Networks: A Survey","created":"2023-04-12","cited_by_count":15,"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":"2408.13659","title":"ReactZyme: A Benchmark for Enzyme-Reaction Prediction","created":"2024-08-24","cited_by_count":13,"area":null},{"id":"2306.13339","title":"TrustGuard: GNN-based Robust and Explainable Trust Evaluation with Dynamicity Support","created":"2023-06-23","cited_by_count":12,"area":null},{"id":"2307.07649","title":"DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training","created":"2023-07-14","cited_by_count":12,"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":"2110.02880","title":"Space-Time Graph Neural Networks","created":"2021-10-06","cited_by_count":10,"area":null},{"id":"2206.15174","title":"Graph-Time Convolutional Neural Networks: Architecture and Theoretical Analysis","created":"2022-06-30","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":"2207.11175","title":"Explaining Dynamic Graph Neural Networks via Relevance Back-propagation","created":"2022-07-22","cited_by_count":8,"area":null},{"id":"2403.00178","title":"Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systems","created":"2024-02-29","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":"2407.03953","title":"Generalizing Graph Transformers Across Diverse Graphs and Tasks via Pre-training","created":"2024-07-04","cited_by_count":7,"area":null},{"id":"2301.04224","title":"Pix2Map: Cross-modal Retrieval for Inferring Street Maps from Images","created":"2023-01-10","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":"2101.05988","title":"Coarse-grained decomposition and fine-grained interaction for multi-hop question answering","created":"2021-01-15","cited_by_count":5,"area":null},{"id":"2210.00032","title":"Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphs","created":"2022-09-30","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":"2410.09590","title":"Bayesian Sheaf Neural Networks","created":"2024-10-12","cited_by_count":5,"area":null},{"id":"2302.06114","title":"A Comprehensive Survey on Graph Summarization with Graph Neural Networks","created":"2023-02-13","cited_by_count":4,"area":null},{"id":"2401.07516","title":"Temporal Link Prediction Using Graph Embedding Dynamics","created":"2024-01-15","cited_by_count":4,"area":null},{"id":"2404.14642","title":"Uncertainty Quantification on Graph Learning: A Survey","created":"2024-04-23","cited_by_count":4,"area":null},{"id":"2106.10656","title":"TD-GEN: Graph Generation With Tree Decomposition","created":"2021-06-20","cited_by_count":3,"area":null},{"id":"2112.07823","title":"Bayesian Graph Contrastive Learning","created":"2021-12-15","cited_by_count":3,"area":null},{"id":"2203.08149","title":"MoReL: Multi-omics Relational Learning","created":"2022-03-15","cited_by_count":3,"area":null},{"id":"2205.09801","title":"Representation Power of Graph Neural Networks: Improved Expressivity via Algebraic Analysis","created":"2022-05-19","cited_by_count":3,"area":null},{"id":"2302.07243","title":"A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder Identification","created":"2023-02-14","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":"2202.08418","title":"Neural Marionette: Unsupervised Learning of Motion Skeleton and Latent Dynamics from Volumetric Video","created":"2022-02-17","cited_by_count":2,"area":null},{"id":"2307.12994","title":"Multi-representations Space Separation based Graph-level Anomaly-aware Detection","created":"2023-07-22","cited_by_count":2,"area":null},{"id":"2410.00665","title":"Graph-Based Representation Learning of Neuronal Dynamics and Behavior","created":"2024-10-01","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":"2505.11930","title":"The Logical Expressiveness of Temporal GNNs via Two-Dimensional Product Logics","created":"2025-05-17","cited_by_count":2,"area":null},{"id":"2106.11430","title":"ConvDySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention and Convolutional Neural Networks","created":"2021-06-21","cited_by_count":1,"area":null},{"id":"2201.01384","title":"Sparse-Dyn: Sparse Dynamic Graph Multi-representation Learning via Event-based Sparse Temporal Attention Network","created":"2022-01-04","cited_by_count":1,"area":null},{"id":"2301.11408","title":"DBGDGM: Dynamic Brain Graph Deep Generative Model","created":"2023-01-26","cited_by_count":1,"area":null},{"id":"2406.16552","title":"Inference of Sequential Patterns for Neural Message Passing in Temporal Graphs","created":"2024-06-24","cited_by_count":1,"area":null},{"id":"2411.07123","title":"Fast and Robust Contextual Node Representation Learning over Dynamic Graphs","created":"2024-11-11","cited_by_count":1,"area":null},{"id":"2503.20281","title":"Are We There Yet? Unraveling the State-of-the-Art Graph Network Intrusion Detection Systems","created":"2025-03-26","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":"2508.07106","title":"BrainATCL: Adaptive Temporal Brain Connectivity Learning for Functional Link Prediction and Age Estimation","created":"2025-08-09","cited_by_count":1,"area":null},{"id":"2512.22772","title":"GRExplainer: A Universal Explanation Method for Temporal Graph Neural Networks","created":"2025-12-28","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":"2606.22914","title":"PromptDyG: Test-Time Prompt Adaptation on Dynamic Graphs","created":"2026-06-22","cited_by_count":1,"area":null},{"id":"2103.07080","title":"DynACPD Embedding Algorithm for Prediction Tasks in Dynamic Networks","created":"2021-03-12","cited_by_count":0,"area":null},{"id":"2106.00258","title":"Divide and Rule: Recurrent Partitioned Network for Dynamic Processes","created":"2021-06-01","cited_by_count":0,"area":null},{"id":"2203.15009","title":"DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series","created":"2022-03-28","cited_by_count":0,"area":null},{"id":"2210.11711","title":"Modelling Multi-relations for Convolutional-based Knowledge Graph Embedding","created":"2022-10-21","cited_by_count":0,"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":"2211.02396","title":"Rethinking the positive role of cluster structure in complex networks for link prediction tasks","created":"2022-11-04","cited_by_count":0,"area":null},{"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":"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":"2408.12753","title":"Contrastive Representation Learning for Dynamic Link Prediction in Temporal Networks","created":"2024-08-22","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":"2501.02981","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","created":"2025-01-06","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":"2505.09331","title":"MUST: Multi-Scale Structural-Temporal Link Prediction Model for UAV Ad Hoc Networks","created":"2025-05-14","cited_by_count":0,"area":null},{"id":"2509.03319","title":"Temporal social network modeling of mobile connectivity data with graph neural networks","created":"2025-09-03","cited_by_count":0,"area":null},{"id":"2602.12613","title":"Coden: Efficient Temporal Graph Neural Networks for Continuous Prediction","created":"2026-02-13","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":"2606.21940","title":"DevoTG: Temporal Graph Neural Networks for Modeling C. elegans Developmental Connectomics","created":"2026-06-20","cited_by_count":0,"area":null},{"id":"2609.25814","title":"CacheDyG: Decoupling Temporal Propagation for Efficient Dynamic Graph Learning","created":"2026-09-22","cited_by_count":0,"area":null},{"id":"2610.03000","title":"Temporal Geometry of Deep Networks: Hyperbolic Representations of Training Dynamics for Intrinsic Explainability","created":"2026-10-02","cited_by_count":0,"area":null}],"next_cursor":null}