{"total":393,"rows":[{"id":"2308.09687","title":"Graph of Thoughts: Solving Elaborate Problems with Large Language Models","created":"2023-08-18","cited_by_count":886,"area":"lr"},{"id":"2101.00797","title":"Beyond Low-frequency Information in Graph Convolutional Networks","created":"2021-01-04","cited_by_count":362,"area":null},{"id":"2103.07719","title":"Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting","created":"2021-03-13","cited_by_count":299,"area":null},{"id":"2101.11174","title":"Graph Neural Network for Traffic Forecasting: A Survey","created":"2021-01-27","cited_by_count":261,"area":null},{"id":"2106.05819","title":"Adversarial Graph Augmentation to Improve Graph Contrastive Learning","created":"2021-06-10","cited_by_count":200,"area":null},{"id":"2101.06861","title":"Discrete Graph Structure Learning for Forecasting Multiple Time Series","created":"2021-01-18","cited_by_count":171,"area":null},{"id":"2202.07179","title":"G-Mixup: Graph Data Augmentation for Graph Classification","created":"2022-02-15","cited_by_count":146,"area":null},{"id":"2105.00696","title":"Graph Learning: A Survey","created":"2021-05-03","cited_by_count":141,"area":null},{"id":"2101.11859","title":"Interpreting and Unifying Graph Neural Networks with An Optimization Framework","created":"2021-01-28","cited_by_count":131,"area":null},{"id":"2211.11695","title":"Disentangled Representation Learning","created":"2022-11-21","cited_by_count":112,"area":null},{"id":"2104.00706","title":"BRepNet: A topological message passing system for solid models","created":"2021-04-01","cited_by_count":95,"area":null},{"id":"2106.06586","title":"Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns","created":"2021-06-11","cited_by_count":95,"area":null},{"id":"2106.03535","title":"Graph Neural Networks in Network Neuroscience","created":"2021-06-07","cited_by_count":92,"area":null},{"id":"2205.07424","title":"Trustworthy Graph Neural Networks: Aspects, Methods and Trends","created":"2022-05-16","cited_by_count":89,"area":null},{"id":"2103.05045","title":"Size-Invariant Graph Representations for Graph Classification Extrapolations","created":"2021-03-08","cited_by_count":84,"area":null},{"id":"2209.12643","title":"PiFold: Toward effective and efficient protein inverse folding","created":"2022-09-22","cited_by_count":83,"area":null},{"id":"2302.02591","title":"Generative Diffusion Models on Graphs: Methods and Applications","created":"2023-02-06","cited_by_count":81,"area":"diffusion"},{"id":"2104.04883","title":"Graph Representation Learning in Biomedicine","created":"2021-04-11","cited_by_count":80,"area":null},{"id":"2206.07579","title":"A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions","created":"2022-06-15","cited_by_count":73,"area":null},{"id":"2304.05055","title":"A Comprehensive Survey on Deep Graph Representation Learning","created":"2023-04-11","cited_by_count":72,"area":null},{"id":"2206.02886","title":"Graph Rationalization with Environment-based Augmentations","created":"2022-06-06","cited_by_count":70,"area":null},{"id":"2203.06714","title":"A Survey on Deep Graph Generation: Methods and Applications","created":"2022-03-13","cited_by_count":69,"area":null},{"id":"2104.14210","title":"FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning","created":"2021-04-29","cited_by_count":68,"area":null},{"id":"2105.13137","title":"Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future","created":"2021-05-27","cited_by_count":67,"area":null},{"id":"2112.10063","title":"Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation","created":"2021-12-19","cited_by_count":67,"area":"distill"},{"id":"2204.01855","title":"A Survey on Graph Representation Learning Methods","created":"2022-04-04","cited_by_count":66,"area":null},{"id":"2104.10482","title":"GraphSVX: Shapley Value Explanations for Graph Neural Networks","created":"2021-04-18","cited_by_count":64,"area":null},{"id":"2101.10025","title":"A Review of Graph Neural Networks and Their Applications in Power Systems","created":"2021-01-25","cited_by_count":63,"area":null},{"id":"2401.14295","title":"Demystifying Chains, Trees, and Graphs of Thoughts","created":"2024-01-25","cited_by_count":63,"area":"lr"},{"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":"2104.13030","title":"A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation","created":"2021-04-27","cited_by_count":59,"area":null},{"id":"2203.15793","title":"Instance Relation Graph Guided Source-Free Domain Adaptive Object Detection","created":"2022-03-29","cited_by_count":55,"area":null},{"id":"2112.08331","title":"Model Stealing Attacks Against Inductive Graph Neural Networks","created":"2021-12-15","cited_by_count":50,"area":null},{"id":"2112.08541","title":"BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing","created":"2021-12-16","cited_by_count":50,"area":null},{"id":"2203.00949","title":"GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation","created":"2022-03-02","cited_by_count":46,"area":null},{"id":"2104.12840","title":"AdaGNN: Graph Neural Networks with Adaptive Frequency Response Filter","created":"2021-04-26","cited_by_count":43,"area":null},{"id":"2206.11972","title":"Task-Adaptive Few-shot Node Classification","created":"2022-06-23","cited_by_count":43,"area":null},{"id":"2211.10738","title":"Knowledge Graph Contrastive Learning Based on Relation-Symmetrical Structure","created":"2022-11-19","cited_by_count":43,"area":null},{"id":"2107.00272","title":"A Survey on Graph-Based Deep Learning for Computational Histopathology","created":"2021-07-01","cited_by_count":41,"area":null},{"id":"2305.08048","title":"Towards Understanding the Generalization of Graph Neural Networks","created":"2023-05-14","cited_by_count":41,"area":null},{"id":"2103.05872","title":"Sampling methods for efficient training of graph convolutional networks: A survey","created":"2021-03-10","cited_by_count":40,"area":null},{"id":"2202.01079","title":"AlphaDesign: A graph protein design method and benchmark on AlphaFoldDB","created":"2022-02-01","cited_by_count":40,"area":null},{"id":"2103.00742","title":"Automated Machine Learning on Graphs: A Survey","created":"2021-03-01","cited_by_count":39,"area":null},{"id":"2210.05801","title":"Linkless Link Prediction via Relational Distillation","created":"2022-10-11","cited_by_count":39,"area":null},{"id":"2104.07886","title":"Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks","created":"2021-04-16","cited_by_count":38,"area":null},{"id":"2108.00261","title":"ECLARE: Extreme Classification with Label Graph Correlations","created":"2021-07-31","cited_by_count":38,"area":null},{"id":"2201.11349","title":"Confidence May Cheat: Self-Training on Graph Neural Networks under Distribution Shift","created":"2022-01-27","cited_by_count":38,"area":null},{"id":"2102.10255","title":"Link Prediction with Persistent Homology: An Interactive View","created":"2021-02-20","cited_by_count":37,"area":null},{"id":"2106.02930","title":"Spectral Temporal Graph Neural Network for Trajectory Prediction","created":"2021-06-05","cited_by_count":36,"area":null},{"id":"2110.14416","title":"Transformers Generalize DeepSets and Can be Extended to Graphs and Hypergraphs","created":"2021-10-27","cited_by_count":35,"area":null},{"id":"2304.01565","title":"A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Material","created":"2023-04-04","cited_by_count":35,"area":"diffusion"},{"id":"2207.00012","title":"Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN","created":"2022-06-30","cited_by_count":34,"area":null},{"id":"2202.10720","title":"EIGNN: Efficient Infinite-Depth Graph Neural Networks","created":"2022-02-22","cited_by_count":32,"area":null},{"id":"2204.06504","title":"DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization","created":"2022-04-13","cited_by_count":32,"area":null},{"id":"2211.00216","title":"Distributed Graph Neural Network Training: A Survey","created":"2022-11-01","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":"2111.01495","title":"Constructing Neural Network-Based Models for Simulating Dynamical Systems","created":"2021-11-02","cited_by_count":31,"area":null},{"id":"2112.03587","title":"TCGL: Temporal Contrastive Graph for Self-supervised Video Representation Learning","created":"2021-12-07","cited_by_count":31,"area":null},{"id":"2208.08302","title":"Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashing","created":"2022-08-17","cited_by_count":31,"area":null},{"id":"2202.10688","title":"Graph Lifelong Learning: A Survey","created":"2022-02-22","cited_by_count":30,"area":null},{"id":"2209.07754","title":"On the Robustness of Graph Neural Diffusion to Topology Perturbations","created":"2022-09-16","cited_by_count":30,"area":null},{"id":"2205.09702","title":"Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis","created":"2022-05-19","cited_by_count":29,"area":null},{"id":"2203.15935","title":"Graph Neural Networks in IoT: A Survey","created":"2022-03-29","cited_by_count":28,"area":null},{"id":"2304.11431","title":"A Review of Deep Learning for Video Captioning","created":"2023-04-22","cited_by_count":28,"area":null},{"id":"2110.15529","title":"Topological Relational Learning on Graphs","created":"2021-10-29","cited_by_count":27,"area":null},{"id":"2303.02448","title":"DAG Matters! GFlowNets Enhanced Explainer For Graph Neural Networks","created":"2023-03-04","cited_by_count":27,"area":null},{"id":"2308.08235","title":"The Expressive Power of Graph Neural Networks: A Survey","created":"2023-08-16","cited_by_count":27,"area":null},{"id":"2202.04822","title":"Survey on Graph Neural Network Acceleration: An Algorithmic Perspective","created":"2022-02-10","cited_by_count":26,"area":null},{"id":"2206.04361","title":"Model Degradation Hinders Deep Graph Neural Networks","created":"2022-06-09","cited_by_count":26,"area":null},{"id":"2209.07807","title":"Model Inversion Attacks against Graph Neural Networks","created":"2022-09-16","cited_by_count":26,"area":null},{"id":"2206.09166","title":"NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search","created":"2022-06-18","cited_by_count":25,"area":null},{"id":"2102.12612","title":"Simultaneously Reconciled Quantile Forecasting of Hierarchically Related Time Series","created":"2021-02-25","cited_by_count":24,"area":null},{"id":"2206.07258","title":"CLNode: Curriculum Learning for Node Classification","created":"2022-06-15","cited_by_count":24,"area":null},{"id":"2304.04300","title":"Class-Imbalanced Learning on Graphs: A Survey","created":"2023-04-09","cited_by_count":24,"area":null},{"id":"2310.06396","title":"Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach","created":"2023-10-10","cited_by_count":24,"area":null},{"id":"2110.14854","title":"RIM: Reliable Influence-based Active Learning on Graphs","created":"2021-10-28","cited_by_count":23,"area":null},{"id":"2206.09144","title":"Beyond Real-world Benchmark Datasets: An Empirical Study of Node Classification with GNNs","created":"2022-06-18","cited_by_count":23,"area":null},{"id":"2304.05059","title":"Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification","created":"2023-04-11","cited_by_count":23,"area":null},{"id":"2202.03813","title":"Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters","created":"2022-02-08","cited_by_count":22,"area":null},{"id":"2106.02533","title":"Graph-based Deep Learning for Communication Networks: A Survey","created":"2021-06-04","cited_by_count":21,"area":null},{"id":"2204.03080","title":"Graph Neural Networks Designed for Different Graph Types: A Survey","created":"2022-04-06","cited_by_count":21,"area":null},{"id":"2208.09151","title":"Ginex: SSD-enabled Billion-scale Graph Neural Network Training on a Single Machine via Provably Optimal In-memory Caching","created":"2022-08-19","cited_by_count":21,"area":null},{"id":"2112.03328","title":"Learning Connectivity with Graph Convolutional Networks for Skeleton-based Action Recognition","created":"2021-12-06","cited_by_count":20,"area":null},{"id":"2303.06471","title":"Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Review","created":"2023-03-11","cited_by_count":20,"area":null},{"id":"2103.16701","title":"Charged particle tracking via edge-classifying interaction networks","created":"2021-03-30","cited_by_count":19,"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":"2108.00955","title":"Evaluating Deep Graph Neural Networks","created":"2021-08-02","cited_by_count":19,"area":null},{"id":"2201.08265","title":"Cross-Domain Few-Shot Graph Classification","created":"2022-01-20","cited_by_count":19,"area":null},{"id":"2207.09179","title":"SCARA: Scalable Graph Neural Networks with Feature-Oriented Optimization","created":"2022-07-19","cited_by_count":19,"area":null},{"id":"2306.04265","title":"Permutation Equivariant Graph Framelets for Heterophilous Graph Learning","created":"2023-06-07","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":"2101.07671","title":"Edge-Featured Graph Attention Network","created":"2021-01-19","cited_by_count":18,"area":null},{"id":"2106.00761","title":"Motif Prediction with Graph Neural Networks","created":"2021-05-26","cited_by_count":18,"area":null},{"id":"2301.12230","title":"Continual Graph Learning: A Survey","created":"2023-01-28","cited_by_count":18,"area":null},{"id":"2403.05075","title":"Benchmarking Large Language Models for Molecule Prediction Tasks","created":"2024-03-08","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":"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":"2203.09258","title":"Explainability in Graph Neural Networks: An Experimental Survey","created":"2022-03-17","cited_by_count":16,"area":null},{"id":"2307.00755","title":"Graph-level Anomaly Detection via Hierarchical Memory Networks","created":"2023-07-03","cited_by_count":16,"area":null},{"id":"2104.04987","title":"AutoGL: A Library for Automated Graph Learning","created":"2021-04-11","cited_by_count":15,"area":null}],"next_cursor":"100"}