{"total":62,"rows":[{"id":"2206.07746","title":"Condensing Graphs via One-Step Gradient Matching","created":"2022-06-15","cited_by_count":100,"area":null},{"id":"2206.02886","title":"Graph Rationalization with Environment-based Augmentations","created":"2022-06-06","cited_by_count":70,"area":null},{"id":"2210.03561","title":"Empowering Graph Representation Learning with Test-Time Graph Transformation","created":"2022-10-07","cited_by_count":67,"area":null},{"id":"2212.04481","title":"A Survey of Graph Neural Networks for Social Recommender Systems","created":"2022-12-08","cited_by_count":65,"area":null},{"id":"2402.03358","title":"A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation","created":"2024-01-29","cited_by_count":57,"area":null},{"id":"2212.00535","title":"Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View","created":"2022-12-01","cited_by_count":55,"area":null},{"id":"2405.09591","title":"A Comprehensive Survey on Data Augmentation","created":"2024-05-15","cited_by_count":40,"area":null},{"id":"2210.05801","title":"Linkless Link Prediction via Relational Distillation","created":"2022-10-11","cited_by_count":39,"area":null},{"id":"2211.02843","title":"Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift","created":"2022-11-05","cited_by_count":38,"area":null},{"id":"2210.02016","title":"Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization","created":"2022-10-05","cited_by_count":35,"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":"2310.00800","title":"GraphPatcher: Mitigating Degree Bias for Graph Neural Networks via Test-time Augmentation","created":"2023-10-01","cited_by_count":31,"area":null},{"id":"2202.13248","title":"Automated Data Augmentations for Graph Classification","created":"2022-02-26","cited_by_count":20,"area":null},{"id":"2206.07743","title":"Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective","created":"2022-06-15","cited_by_count":19,"area":null},{"id":"2303.10108","title":"Data-Centric Learning from Unlabeled Graphs with Diffusion Model","created":"2023-03-17","cited_by_count":19,"area":"diffusion"},{"id":"2302.05044","title":"Toward Degree Bias in Embedding-Based Knowledge Graph Completion","created":"2023-02-10","cited_by_count":17,"area":null},{"id":"2410.19265","title":"A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation","created":"2024-10-25","cited_by_count":17,"area":null},{"id":"2208.09126","title":"GraphTTA: Test Time Adaptation on Graph Neural Networks","created":"2022-08-19","cited_by_count":14,"area":null},{"id":"2305.12087","title":"Semi-Supervised Graph Imbalanced Regression","created":"2023-05-20","cited_by_count":14,"area":null},{"id":"2210.15721","title":"GraphMAD: Graph Mixup for Data Augmentation using Data-Driven Convex Clustering","created":"2022-10-27","cited_by_count":13,"area":null},{"id":"2211.15899","title":"FakeEdge: Alleviate Dataset Shift in Link Prediction","created":"2022-11-29","cited_by_count":12,"area":null},{"id":"2211.14394","title":"Link Prediction with Non-Contrastive Learning","created":"2022-11-25","cited_by_count":11,"area":null},{"id":"2212.09970","title":"Data Augmentation on Graphs: A Technical Survey","created":"2022-12-20","cited_by_count":10,"area":null},{"id":"2412.10434","title":"NAT-NL2GQL: A Novel Multi-Agent Framework for Translating Natural Language to Graph Query Language","created":"2024-12-11","cited_by_count":9,"area":"agents"},{"id":"2301.02780","title":"Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching","created":"2023-01-07","cited_by_count":8,"area":null},{"id":"2211.01214","title":"Time-aware Random Walk Diffusion to Improve Dynamic Graph Learning","created":"2022-11-02","cited_by_count":7,"area":null},{"id":"2306.06936","title":"CARL-G: Clustering-Accelerated Representation Learning on Graphs","created":"2023-06-12","cited_by_count":6,"area":null},{"id":"2404.08660","title":"How Does Message Passing Improve Collaborative Filtering?","created":"2024-03-27","cited_by_count":5,"area":null},{"id":"2310.00277","title":"A Unified Framework for Generative Data Augmentation: A Comprehensive Survey","created":"2023-09-30","cited_by_count":4,"area":null},{"id":"2402.09711","title":"Node Duplication Improves Cold-start Link Prediction","created":"2024-02-15","cited_by_count":4,"area":null},{"id":"2503.12563","title":"Diffusion on Graph: Augmentation of Graph Structure for Node Classification","created":"2025-03-16","cited_by_count":4,"area":"diffusion"},{"id":"2606.24418","title":"Data Augmentation: A Fourier Analysis Perspective","created":"2026-06-23","cited_by_count":4,"area":null},{"id":"2210.10946","title":"Causally-guided Regularization of Graph Attention Improves Generalizability","created":"2022-10-20","cited_by_count":3,"area":null},{"id":"2305.13656","title":"Link Prediction without Graph Neural Networks","created":"2023-05-23","cited_by_count":3,"area":null},{"id":"2310.03977","title":"Perfect Alignment May be Poisonous to Graph Contrastive Learning","created":"2023-10-06","cited_by_count":3,"area":null},{"id":"2204.10390","title":"SoftEdge: Regularizing Graph Classification with Random Soft Edges","created":"2022-04-21","cited_by_count":2,"area":null},{"id":"2210.03123","title":"On the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language Processing","created":"2022-10-06","cited_by_count":2,"area":null},{"id":"2306.07737","title":"Robustness and Generalization Performance of Deep Learning Models on Cyber-Physical Systems: A Comparative Study","created":"2023-06-13","cited_by_count":2,"area":null},{"id":"2401.09953","title":"Through the Dual-Prism: A Spectral Perspective on Graph Data Augmentation for Graph Classification","created":"2024-01-18","cited_by_count":2,"area":null},{"id":"2403.07943","title":"Revisiting Edge Perturbation for Graph Neural Network in Graph Data Augmentation and Attack","created":"2024-03-10","cited_by_count":2,"area":null},{"id":"2506.01728","title":"Principled Data Augmentation for Learning to Solve Quadratic Programming Problems","created":"2025-06-02","cited_by_count":2,"area":null},{"id":"2304.12751","title":"Centrality-Based Node Feature Augmentation for Robust Network Alignment","created":"2023-04-25","cited_by_count":1,"area":null},{"id":"2405.19600","title":"Rethinking Spectral Augmentation for Contrast-based Graph Self-Supervised Learning","created":"2024-05-30","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":"2411.08909","title":"Long-context Protein Language Modeling Using Bidirectional Mamba with Shared Projection Layers","created":"2024-10-29","cited_by_count":1,"area":"ssm"},{"id":"2412.01163","title":"Graph Community Augmentation with GMM-based Modeling in Latent Space","created":"2024-12-02","cited_by_count":1,"area":null},{"id":"2204.11164","title":"Are Your Reviewers Being Treated Equally? Discovering Subgroup Structures to Improve Fairness in Spam Detection","created":"2022-04-24","cited_by_count":0,"area":null},{"id":"2207.05301","title":"Edge Augmentation on Disconnected Graphs via Eigenvalue Elevation","created":"2022-07-12","cited_by_count":0,"area":null},{"id":"2303.13757","title":"Structural Imbalance Aware Graph Augmentation Learning","created":"2023-03-24","cited_by_count":0,"area":null},{"id":"2309.10134","title":"GDM: Dual Mixup for Graph Classification with Limited Supervision","created":"2023-09-18","cited_by_count":0,"area":null},{"id":"2405.03950","title":"Relating-Up: Advancing Graph Neural Networks through Inter-Graph Relationships","created":"2024-05-07","cited_by_count":0,"area":null},{"id":"2409.00421","title":"Reproducibility Study Of Learning Fair Graph Representations Via Automated Data Augmentations","created":"2024-08-31","cited_by_count":0,"area":null},{"id":"2412.05312","title":"Self-Supervised Learning for Graph-Structured Data in Healthcare Applications: A Comprehensive Review","created":"2024-11-28","cited_by_count":0,"area":null},{"id":"2509.22017","title":"AEGIS: Authentic Edge Growth In Sparsity for Link Prediction in Edge-Sparse Bipartite Knowledge Graphs","created":"2025-09-26","cited_by_count":0,"area":null},{"id":"2601.10150","title":"Simple Network Graph Comparative Learning","created":"2026-01-15","cited_by_count":0,"area":null},{"id":"2602.09437","title":"Diffusion-Guided Pretraining for Brain Graph Foundation Models","created":"2026-02-10","cited_by_count":0,"area":null},{"id":"2605.03076","title":"AdNGCL: Adaptive Negative Scheduling for Graph Contrastive Learning","created":"2026-05-04","cited_by_count":0,"area":null},{"id":"2605.06931","title":"Target-Aware Data Augmentation for SAT Prediction","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2606.12581","title":"Graph Reduction in Multirelational Networks: A Spreading-Oriented Reduction Benchmark","created":"2026-06-10","cited_by_count":0,"area":null},{"id":"2606.19770","title":"An Information Theoretic Framework for Graph Novelty Generation via Latent Mixture Modeling","created":"2026-06-18","cited_by_count":0,"area":null},{"id":"2610.05653","title":"Graph Data Augmentation via Contrastive Generator Inversion ($\\texttt{DCBA}$)","created":"2026-10-05","cited_by_count":0,"area":null},{"id":"2610.10305","title":"How Do Transformers Learn to Represent Symmetries?","created":"2026-10-07","cited_by_count":0,"area":null}],"next_cursor":null}