{"total":241,"rows":[{"id":"2110.07875","title":"Graph Neural Networks with Learnable Structural and Positional Representations","created":"2021-10-15","cited_by_count":333,"area":null},{"id":"2106.05234","title":"Do Transformers Really Perform Bad for Graph Representation?","created":"2021-06-09","cited_by_count":320,"area":null},{"id":"2308.07134","title":"Language is All a Graph Needs","created":"2023-08-14","cited_by_count":220,"area":null},{"id":"2202.03036","title":"Structure-Aware Transformer for Graph Representation Learning","created":"2022-02-07","cited_by_count":194,"area":null},{"id":"2112.14936","title":"Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks","created":"2021-12-30","cited_by_count":176,"area":null},{"id":"2208.06677","title":"Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models","created":"2022-08-13","cited_by_count":174,"area":null},{"id":"2106.07476","title":"Training Graph Neural Networks with 1000 Layers","created":"2021-06-14","cited_by_count":169,"area":null},{"id":"2110.13197","title":"Nested Graph Neural Networks","created":"2021-10-25","cited_by_count":137,"area":null},{"id":"2307.10802","title":"Meta-Transformer: A Unified Framework for Multimodal Learning","created":"2023-07-20","cited_by_count":136,"area":null},{"id":"2102.06790","title":"A Unified Lottery Ticket Hypothesis for Graph Neural Networks","created":"2021-02-12","cited_by_count":118,"area":null},{"id":"2108.03348","title":"Global Self-Attention as a Replacement for Graph Convolution","created":"2021-08-07","cited_by_count":114,"area":null},{"id":"2112.00431","title":"MAD: A Scalable Dataset for Language Grounding in Videos from Movie Audio Descriptions","created":"2021-12-01","cited_by_count":108,"area":null},{"id":"2107.02392","title":"Dirichlet Energy Constrained Learning for Deep Graph Neural Networks","created":"2021-07-06","cited_by_count":97,"area":null},{"id":"2201.07858","title":"Decoupling the Depth and Scope of Graph Neural Networks","created":"2022-01-19","cited_by_count":94,"area":null},{"id":"2309.00367","title":"Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark","created":"2023-09-01","cited_by_count":85,"area":null},{"id":"2406.08993","title":"Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification","created":"2024-06-13","cited_by_count":85,"area":null},{"id":"2206.04355","title":"Graph Attention Multi-Layer Perceptron","created":"2022-06-09","cited_by_count":83,"area":null},{"id":"2202.00661","title":"When Do Flat Minima Optimizers Work?","created":"2022-02-01","cited_by_count":78,"area":null},{"id":"2210.01765","title":"One Transformer Can Understand Both 2D & 3D Molecular Data","created":"2022-10-04","cited_by_count":77,"area":null},{"id":"2201.12787","title":"GRPE: Relative Positional Encoding for Graph Transformer","created":"2022-01-30","cited_by_count":70,"area":null},{"id":"2302.01524","title":"Ordered GNN: Ordering Message Passing to Deal with Heterophily and Over-smoothing","created":"2023-02-03","cited_by_count":67,"area":null},{"id":"2103.06064","title":"Graph Neural Networks Inspired by Classical Iterative Algorithms","created":"2021-03-10","cited_by_count":60,"area":null},{"id":"2111.04840","title":"Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods","created":"2021-11-08","cited_by_count":58,"area":null},{"id":"2209.13232","title":"A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective","created":"2022-09-27","cited_by_count":56,"area":null},{"id":"2210.07494","title":"A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking","created":"2022-10-14","cited_by_count":56,"area":null},{"id":"2105.04550","title":"Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth","created":"2021-05-10","cited_by_count":55,"area":null},{"id":"2205.13401","title":"Your Transformer May Not be as Powerful as You Expect","created":"2022-05-26","cited_by_count":55,"area":null},{"id":"2507.16991","title":"PyG 2.0: Scalable Learning on Real World Graphs","created":"2025-07-22","cited_by_count":52,"area":null},{"id":"2103.16178","title":"Learnable Graph Matching: Incorporating Graph Partitioning with Deep Feature Learning for Multiple Object Tracking","created":"2021-03-30","cited_by_count":47,"area":null},{"id":"2102.06986","title":"How Framelets Enhance Graph Neural Networks","created":"2021-02-13","cited_by_count":46,"area":null},{"id":"2106.07971","title":"Simple GNN Regularisation for 3D Molecular Property Prediction & Beyond","created":"2021-06-15","cited_by_count":44,"area":null},{"id":"2104.09333","title":"Camera Calibration and Player Localization in SoccerNet-v2 and Investigation of their Representations for Action Spotting","created":"2021-04-19","cited_by_count":43,"area":null},{"id":"2103.13355","title":"Bag of Tricks for Node Classification with Graph Neural Networks","created":"2021-03-24","cited_by_count":40,"area":null},{"id":"2103.04886","title":"Lipschitz Normalization for Self-Attention Layers with Application to Graph Neural Networks","created":"2021-03-08","cited_by_count":39,"area":null},{"id":"2305.18405","title":"Dink-Net: Neural Clustering on Large Graphs","created":"2023-05-28","cited_by_count":38,"area":null},{"id":"2101.03682","title":"MAAS: Multi-modal Assignation for Active Speaker Detection","created":"2021-01-11","cited_by_count":36,"area":null},{"id":"2108.10521","title":"Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study","created":"2021-08-24","cited_by_count":36,"area":null},{"id":"2108.12238","title":"Group-Aware Graph Neural Network for Nationwide City Air Quality Forecasting","created":"2021-08-27","cited_by_count":32,"area":null},{"id":"2407.09777","title":"Graph Transformers: A Survey","created":"2024-07-13","cited_by_count":32,"area":null},{"id":"2110.01717","title":"Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs","created":"2021-09-30","cited_by_count":31,"area":null},{"id":"2210.00102","title":"MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization","created":"2022-09-30","cited_by_count":31,"area":null},{"id":"2108.08046","title":"Variational Graph Normalized Auto-Encoders","created":"2021-08-18","cited_by_count":29,"area":null},{"id":"2103.00794","title":"Early-Bird GCNs: Graph-Network Co-Optimization Towards More Efficient GCN Training and Inference via Drawing Early-Bird Lottery Tickets","created":"2021-03-01","cited_by_count":27,"area":null},{"id":"2106.10234","title":"Dual-view Molecule Pre-training","created":"2021-06-17","cited_by_count":27,"area":null},{"id":"2109.12283","title":"Scalable deeper graph neural networks for high-performance materials property prediction","created":"2021-09-25","cited_by_count":26,"area":null},{"id":"2109.12872","title":"Meta-Aggregator: Learning to Aggregate for 1-bit Graph Neural Networks","created":"2021-09-27","cited_by_count":25,"area":null},{"id":"2111.04718","title":"Directional Message Passing on Molecular Graphs via Synthetic Coordinates","created":"2021-11-08","cited_by_count":24,"area":null},{"id":"2112.07160","title":"A New Perspective on the Effects of Spectrum in Graph Neural Networks","created":"2021-12-14","cited_by_count":24,"area":null},{"id":"2201.05493","title":"Contrastive Laplacian Eigenmaps","created":"2022-01-14","cited_by_count":24,"area":null},{"id":"2205.11691","title":"High-Order Pooling for Graph Neural Networks with Tensor Decomposition","created":"2022-05-24","cited_by_count":24,"area":null},{"id":"2306.02376","title":"Towards Deep Attention in Graph Neural Networks: Problems and Remedies","created":"2023-06-04","cited_by_count":24,"area":null},{"id":"2310.10603","title":"Exploring the Power of Graph Neural Networks in Solving Linear Optimization Problems","created":"2023-10-16","cited_by_count":24,"area":null},{"id":"2309.15123","title":"Uncovering Neural Scaling Laws in Molecular Representation Learning","created":"2023-09-15","cited_by_count":23,"area":null},{"id":"2302.10407","title":"Label Information Enhanced Fraud Detection against Low Homophily in Graphs","created":"2023-02-21","cited_by_count":22,"area":null},{"id":"2303.16982","title":"Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+","created":"2023-03-16","cited_by_count":22,"area":null},{"id":"2402.05962","title":"EXGC: Bridging Efficiency and Explainability in Graph Condensation","created":"2024-02-05","cited_by_count":20,"area":null},{"id":"2107.10234","title":"Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks","created":"2021-07-21","cited_by_count":19,"area":null},{"id":"2210.02636","title":"Geodesic Graph Neural Network for Efficient Graph Representation Learning","created":"2022-10-06","cited_by_count":19,"area":null},{"id":"2108.09638","title":"Signed Bipartite Graph Neural Networks","created":"2021-08-22","cited_by_count":18,"area":null},{"id":"2111.06483","title":"Sequential Aggregation and Rematerialization: Distributed Full-batch Training of Graph Neural Networks on Large Graphs","created":"2021-11-11","cited_by_count":18,"area":null},{"id":"2401.00529","title":"GraphGPT: Generative Pre-trained Graph Eulerian Transformer","created":"2023-12-31","cited_by_count":17,"area":null},{"id":"2404.17099","title":"Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND","created":"2024-04-26","cited_by_count":17,"area":null},{"id":"2211.05368","title":"A Comprehensive Survey on Distributed Training of Graph Neural Networks","created":"2022-11-10","cited_by_count":15,"area":null},{"id":"2303.00579","title":"Are More Layers Beneficial to Graph Transformers?","created":"2023-03-01","cited_by_count":15,"area":null},{"id":"2306.03065","title":"LibAUC: A Deep Learning Library for X-Risk Optimization","created":"2023-06-05","cited_by_count":15,"area":null},{"id":"2103.03113","title":"Towards Deepening Graph Neural Networks: A GNTK-based Optimization Perspective","created":"2021-03-03","cited_by_count":14,"area":null},{"id":"2302.11416","title":"Structure Embedded Nucleus Classification for Histopathology Images","created":"2023-02-22","cited_by_count":14,"area":null},{"id":"2305.13854","title":"The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Survey","created":"2023-05-23","cited_by_count":14,"area":null},{"id":"2308.09517","title":"Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity","created":"2023-08-18","cited_by_count":14,"area":null},{"id":"2407.11060","title":"A review of graph neural network applications in mechanics-related domains","created":"2024-07-10","cited_by_count":14,"area":null},{"id":"2112.11594","title":"GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design","created":"2021-12-22","cited_by_count":13,"area":null},{"id":"2203.15913","title":"Pretraining Graph Neural Networks for few-shot Analog Circuit Modeling and Design","created":"2022-03-29","cited_by_count":13,"area":null},{"id":"2212.08541","title":"Learnable Commutative Monoids for Graph Neural Networks","created":"2022-12-16","cited_by_count":13,"area":null},{"id":"2103.00959","title":"CogDL: A Comprehensive Library for Graph Deep Learning","created":"2021-03-01","cited_by_count":12,"area":null},{"id":"2211.16494","title":"On the Ability of Graph Neural Networks to Model Interactions Between Vertices","created":"2022-11-29","cited_by_count":12,"area":null},{"id":"2402.04538","title":"Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers","created":"2024-02-07","cited_by_count":12,"area":null},{"id":"2205.15653","title":"Label-Enhanced Graph Neural Network for Semi-supervised Node Classification","created":"2022-05-31","cited_by_count":11,"area":null},{"id":"2308.10051","title":"The Snowflake Hypothesis: Training Deep GNN with One Node One Receptive field","created":"2023-08-19","cited_by_count":11,"area":null},{"id":"2106.08551","title":"Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks","created":"2021-06-16","cited_by_count":10,"area":null},{"id":"2109.10683","title":"Adaptive Neural Message Passing for Inductive Learning on Hypergraphs","created":"2021-09-22","cited_by_count":10,"area":null},{"id":"2110.13567","title":"Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks","created":"2021-10-26","cited_by_count":10,"area":null},{"id":"2111.15112","title":"AugLiChem: Data Augmentation Library of Chemical Structures for Machine Learning","created":"2021-11-30","cited_by_count":10,"area":null},{"id":"2201.07537","title":"Graph Neural Network-based Android Malware Classification with Jumping Knowledge","created":"2022-01-19","cited_by_count":9,"area":null},{"id":"2305.11424","title":"Graph Propagation Transformer for Graph Representation Learning","created":"2023-05-19","cited_by_count":9,"area":null},{"id":"2311.15772","title":"Attend Who is Weak: Enhancing Graph Condensation via Cross-Free Adversarial Training","created":"2023-11-27","cited_by_count":9,"area":null},{"id":"2402.12946","title":"Cell Graph Transformer for Nuclei Classification","created":"2024-02-20","cited_by_count":9,"area":null},{"id":"2403.15520","title":"GTC: GNN-Transformer Co-contrastive Learning for Self-supervised Heterogeneous Graph Representation","created":"2024-03-22","cited_by_count":9,"area":null},{"id":"2403.18570","title":"Physics-Informed Graph Neural Networks for Water Distribution Systems","created":"2024-03-27","cited_by_count":9,"area":null},{"id":"2504.07378","title":"BRepFormer: Transformer-Based B-rep Geometric Feature Recognition","created":"2025-04-10","cited_by_count":9,"area":null},{"id":"2111.08279","title":"Keypoint Message Passing for Video-based Person Re-Identification","created":"2021-11-16","cited_by_count":8,"area":null},{"id":"2111.11638","title":"Network In Graph Neural Network","created":"2021-11-23","cited_by_count":8,"area":null},{"id":"2502.12732","title":"Circuit Representation Learning with Masked Gate Modeling and Verilog-AIG Alignment","created":"2025-02-18","cited_by_count":8,"area":null},{"id":"2103.16584","title":"Parameterized Hypercomplex Graph Neural Networks for Graph Classification","created":"2021-03-30","cited_by_count":7,"area":null},{"id":"2110.09344","title":"ifMixup: Interpolating Graph Pair to Regularize Graph Classification","created":"2021-10-18","cited_by_count":7,"area":null},{"id":"2307.08874","title":"Latent Space Representations of Neural Algorithmic Reasoners","created":"2023-07-17","cited_by_count":7,"area":null},{"id":"2309.12067","title":"Survey of Action Recognition, Spotting and Spatio-Temporal Localization in Soccer -- Current Trends and Research Perspectives","created":"2023-09-21","cited_by_count":7,"area":null},{"id":"2404.13344","title":"GRANOLA: Adaptive Normalization for Graph Neural Networks","created":"2024-04-20","cited_by_count":7,"area":null},{"id":"2407.12136","title":"Molecular Topological Profile (MOLTOP) -- Simple and Strong Baseline for Molecular Graph Classification","created":"2024-07-16","cited_by_count":7,"area":null},{"id":"2603.01385","title":"Toward Graph-Tokenizing Large Language Models with Reconstructive Graph Instruction Tuning","created":"2026-03-02","cited_by_count":7,"area":null},{"id":"2103.07877","title":"R-GSN: The Relation-based Graph Similar Network for Heterogeneous Graph","created":"2021-03-14","cited_by_count":6,"area":null}],"next_cursor":"100"}