{"total":142,"rows":[{"id":"2110.08727","title":"Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation","created":"2021-10-17","cited_by_count":152,"area":null},{"id":"2202.08235","title":"Data Augmentation for Deep Graph Learning: A Survey","created":"2022-02-16","cited_by_count":133,"area":null},{"id":"2205.09329","title":"Dataset Pruning: Reducing Training Data by Examining Generalization Influence","created":"2022-05-19","cited_by_count":133,"area":null},{"id":"2301.07014","title":"Dataset Distillation: A Comprehensive Review","created":"2023-01-17","cited_by_count":127,"area":null},{"id":"2206.00240","title":"Privacy for Free: How does Dataset Condensation Help Privacy?","created":"2022-06-01","cited_by_count":120,"area":null},{"id":"2310.05773","title":"Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching","created":"2023-10-09","cited_by_count":118,"area":null},{"id":"2301.05603","title":"A Comprehensive Survey of Dataset Distillation","created":"2023-01-13","cited_by_count":115,"area":null},{"id":"2206.07746","title":"Condensing Graphs via One-Step Gradient Matching","created":"2022-06-15","cited_by_count":100,"area":null},{"id":"2301.04272","title":"Data Distillation: A Survey","created":"2023-01-11","cited_by_count":87,"area":null},{"id":"2306.02664","title":"Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data","created":"2023-06-05","cited_by_count":71,"area":null},{"id":"2310.14664","title":"Data Pruning via Moving-one-Sample-out","created":"2023-10-23","cited_by_count":67,"area":null},{"id":"2202.08871","title":"Graph Data Augmentation for Graph Machine Learning: A Survey","created":"2022-02-17","cited_by_count":62,"area":null},{"id":"2212.06152","title":"Accelerating Dataset Distillation via Model Augmentation","created":"2022-12-12","cited_by_count":62,"area":null},{"id":"2206.03426","title":"Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage","created":"2022-06-07","cited_by_count":61,"area":null},{"id":"2206.13697","title":"Graph Condensation via Receptive Field Distribution Matching","created":"2022-06-28","cited_by_count":60,"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":"2309.09455","title":"CaT: Balanced Continual Graph Learning with Graph Condensation","created":"2023-09-18","cited_by_count":52,"area":null},{"id":"2310.09192","title":"Does Graph Distillation See Like Vision Dataset Counterpart?","created":"2023-10-13","cited_by_count":45,"area":null},{"id":"2207.07888","title":"SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks","created":"2022-07-16","cited_by_count":37,"area":null},{"id":"2310.11046","title":"Fast Graph Condensation with Structure-based Neural Tangent Kernel","created":"2023-10-17","cited_by_count":36,"area":null},{"id":"2305.01975","title":"A Survey on Dataset Distillation: Approaches, Applications and Future Directions","created":"2023-05-03","cited_by_count":32,"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":"2307.15967","title":"Graph Condensation for Inductive Node Representation Learning","created":"2023-07-29","cited_by_count":31,"area":null},{"id":"2204.10037","title":"DropMessage: Unifying Random Dropping for Graph Neural Networks","created":"2022-04-21","cited_by_count":29,"area":null},{"id":"2402.05011","title":"Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching","created":"2024-02-07","cited_by_count":28,"area":null},{"id":"2401.11720","title":"Graph Condensation: A Survey","created":"2024-01-22","cited_by_count":23,"area":null},{"id":"2405.13707","title":"Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition","created":"2024-05-22","cited_by_count":23,"area":null},{"id":"2310.04987","title":"Data-centric Graph Learning: A Survey","created":"2023-10-08","cited_by_count":22,"area":null},{"id":"2206.02626","title":"Infinite Recommendation Networks: A Data-Centric Approach","created":"2022-06-03","cited_by_count":20,"area":null},{"id":"2210.10321","title":"Efficient Bi-Level Optimization for Recommendation Denoising","created":"2022-10-19","cited_by_count":20,"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":"2207.08629","title":"Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networks","created":"2022-07-18","cited_by_count":18,"area":null},{"id":"2306.14052","title":"A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware","created":"2023-06-24","cited_by_count":18,"area":null},{"id":"2401.15863","title":"Importance-Aware Adaptive Dataset Distillation","created":"2024-01-29","cited_by_count":18,"area":null},{"id":"2403.07294","title":"Graph Data Condensation via Self-expressive Graph Structure Reconstruction","created":"2024-03-12","cited_by_count":18,"area":null},{"id":"2304.03344","title":"Graph Collaborative Signals Denoising and Augmentation for Recommendation","created":"2023-04-06","cited_by_count":16,"area":null},{"id":"2310.09202","title":"Graph Distillation with Eigenbasis Matching","created":"2023-10-13","cited_by_count":16,"area":null},{"id":"2306.08854","title":"A Gromov--Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening","created":"2023-06-15","cited_by_count":15,"area":null},{"id":"2309.10979","title":"Towards Data-centric Graph Machine Learning: Review and Outlook","created":"2023-09-20","cited_by_count":15,"area":null},{"id":"2406.15575","title":"Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity","created":"2024-06-21","cited_by_count":14,"area":null},{"id":"2310.09486","title":"Mirage: Model-Agnostic Graph Distillation for Graph Classification","created":"2023-10-14","cited_by_count":13,"area":null},{"id":"2312.15520","title":"Graph Coarsening via Convolution Matching for Scalable Graph Neural Network Training","created":"2023-12-24","cited_by_count":12,"area":null},{"id":"2407.00615","title":"GC-Bench: An Open and Unified Benchmark for Graph Condensation","created":"2024-06-30","cited_by_count":12,"area":null},{"id":"2306.14375","title":"Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networks","created":"2023-06-26","cited_by_count":11,"area":null},{"id":"2401.12231","title":"Disentangled Condensation for Large-scale Graphs","created":"2024-01-18","cited_by_count":10,"area":null},{"id":"2402.01242","title":"Two Heads Are Better Than One: Boosting Graph Sparse Training via Semantic and Topological Awareness","created":"2024-02-02","cited_by_count":10,"area":null},{"id":"2210.00437","title":"A Unified Framework for Optimization-Based Graph Coarsening","created":"2022-10-02","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":"2312.14439","title":"PUMA: Efficient Continual Graph Learning for Node Classification with Graph Condensation","created":"2023-12-22","cited_by_count":9,"area":null},{"id":"2402.02000","title":"A Survey on Graph Condensation","created":"2024-02-03","cited_by_count":9,"area":null},{"id":"2403.07245","title":"Dataset Condensation for Time Series Classification via Dual Domain Matching","created":"2024-03-12","cited_by_count":9,"area":null},{"id":"2405.18127","title":"Graph Coarsening with Message-Passing Guarantees","created":"2024-05-28","cited_by_count":9,"area":null},{"id":"2412.13842","title":"Graph Coarsening via Supervised Granular-Ball for Scalable Graph Neural Network Training","created":"2024-12-18","cited_by_count":9,"area":null},{"id":"2402.04924","title":"Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient Matching","created":"2024-02-07","cited_by_count":8,"area":null},{"id":"2403.14951","title":"Simple Graph Condensation","created":"2024-03-22","cited_by_count":8,"area":null},{"id":"2405.20724","title":"Learning on Large Graphs using Intersecting Communities","created":"2024-05-31","cited_by_count":8,"area":null},{"id":"2406.13200","title":"RobGC: Towards Robust Graph Condensation","created":"2024-06-19","cited_by_count":8,"area":null},{"id":"2502.15054","title":"GiGL: Large-Scale Graph Neural Networks at Snapchat","created":"2025-02-20","cited_by_count":8,"area":null},{"id":"2505.16860","title":"GCAL: Adapting Graph Models to Evolving Domain Shifts","created":"2025-05-22","cited_by_count":8,"area":null},{"id":"2309.09517","title":"FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural Networks","created":"2023-09-18","cited_by_count":7,"area":null},{"id":"2406.16715","title":"GC4NC: A Benchmark Framework for Graph Condensation on Node Classification with New Insights","created":"2024-06-24","cited_by_count":7,"area":null},{"id":"2502.10556","title":"Recent Advances in Malware Detection: Graph Learning and Explainability","created":"2025-02-14","cited_by_count":7,"area":null},{"id":"2205.10652","title":"Are Message Passing Neural Networks Really Helpful for Knowledge Graph Completion?","created":"2022-05-21","cited_by_count":6,"area":null},{"id":"2305.19306","title":"A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks","created":"2023-05-30","cited_by_count":6,"area":null},{"id":"2406.02131","title":"CondTSF: One-line Plugin of Dataset Condensation for Time Series Forecasting","created":"2024-06-04","cited_by_count":6,"area":null},{"id":"2409.08658","title":"Promoting Fairness in Link Prediction with Graph Enhancement","created":"2024-09-13","cited_by_count":6,"area":null},{"id":"2411.17063","title":"Contrastive Graph Condensation: Advancing Data Versatility through Self-Supervised Learning","created":"2024-11-26","cited_by_count":6,"area":null},{"id":"2412.21197","title":"A Large-Scale Study on Video Action Dataset Condensation","created":"2024-12-30","cited_by_count":6,"area":null},{"id":"2502.17614","title":"Scalable Graph Condensation with Evolving Capabilities","created":"2025-02-24","cited_by_count":6,"area":null},{"id":"2503.03995","title":"Subgraph Federated Learning for Local Generalization","created":"2025-03-06","cited_by_count":6,"area":null},{"id":"2505.02573","title":"Rethinking Federated Graph Learning: A Data Condensation Perspective","created":"2025-05-05","cited_by_count":6,"area":null},{"id":"2308.02916","title":"Adversarial Erasing with Pruned Elements: Towards Better Graph Lottery Ticket","created":"2023-08-05","cited_by_count":5,"area":null},{"id":"2405.14246","title":"GCondenser: Benchmarking Graph Condensation","created":"2024-05-23","cited_by_count":5,"area":null},{"id":"2405.17535","title":"Calibrated Dataset Condensation for Faster Hyperparameter Search","created":"2024-05-27","cited_by_count":5,"area":null},{"id":"2406.15042","title":"Behaviour Distillation","created":"2024-06-21","cited_by_count":5,"area":null},{"id":"2407.08064","title":"TinyGraph: Joint Feature and Node Condensation for Graph Neural Networks","created":"2024-07-10","cited_by_count":5,"area":null},{"id":"2407.11025","title":"Backdoor Graph Condensation","created":"2024-07-03","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":"2402.01261","title":"TEDDY: Trimming Edges with Degree-based Discrimination strategY","created":"2024-02-02","cited_by_count":4,"area":null},{"id":"2406.06609","title":"Mitigating Bias in Dataset Distillation","created":"2024-06-06","cited_by_count":4,"area":null},{"id":"2501.02565","title":"Efficient Graph Condensation via Gaussian Process","created":"2025-01-05","cited_by_count":4,"area":null},{"id":"2505.20807","title":"Simple yet Effective Graph Distillation via Clustering","created":"2025-05-27","cited_by_count":4,"area":null},{"id":"2310.09983","title":"Farzi Data: Autoregressive Data Distillation","created":"2023-10-15","cited_by_count":3,"area":null},{"id":"2405.17404","title":"Spectral Greedy Coresets for Graph Neural Networks","created":"2024-05-27","cited_by_count":3,"area":null},{"id":"2502.12608","title":"Unveiling Mode Connectivity in Graph Neural Networks","created":"2025-02-18","cited_by_count":3,"area":null},{"id":"2506.11743","title":"Taxonomy of reduction matrices for Graph Coarsening","created":"2025-06-13","cited_by_count":3,"area":null},{"id":"2506.14570","title":"From Points to Places: Towards Human Mobility-Driven Spatiotemporal Foundation Models via Understanding Places","created":"2025-06-17","cited_by_count":3,"area":null},{"id":"2403.13268","title":"Unifews: You Need Fewer Operations for Efficient Graph Neural Networks","created":"2024-03-20","cited_by_count":2,"area":null},{"id":"2407.16594","title":"Flexible Generation of Preference Data for Recommendation Analysis","created":"2024-07-23","cited_by_count":2,"area":null},{"id":"2501.09821","title":"BN-Pool: Bayesian Nonparametric Pooling for Graphs","created":"2025-01-16","cited_by_count":2,"area":null},{"id":"2503.01212","title":"Understanding Dataset Distillation via Spectral Filtering","created":"2025-03-03","cited_by_count":2,"area":null},{"id":"2504.14188","title":"Rethinking Client-oriented Federated Graph Learning","created":"2025-04-19","cited_by_count":2,"area":null},{"id":"2504.18273","title":"Efficient Learning on Large Graphs using a Densifying Regularity Lemma","created":"2025-04-25","cited_by_count":2,"area":null},{"id":"2504.21296","title":"Fairness in Augmented Graph Learning: A Survey","created":"2025-04-30","cited_by_count":2,"area":null},{"id":"2506.06558","title":"Rapid training of Hamiltonian graph networks using random features","created":"2025-06-06","cited_by_count":2,"area":null},{"id":"2506.19224","title":"GBGC: Efficient and Adaptive Graph Coarsening via Granular-ball Computing","created":"2025-06-24","cited_by_count":2,"area":null},{"id":"2511.20222","title":"Decoupling and Damping: Structurally-Regularized Gradient Matching for Multimodal Graph Condensation","created":"2025-11-25","cited_by_count":2,"area":null},{"id":"2603.10410","title":"Effective Dataset Distillation for Spatio-Temporal Forecasting with Bi-dimensional Compression","created":"2026-03-11","cited_by_count":2,"area":null},{"id":"2312.16542","title":"FALCON: Feature-Label Constrained Graph Net Collapse for Memory Efficient GNNs","created":"2023-12-27","cited_by_count":1,"area":null},{"id":"2407.08108","title":"CADC: Encoding User-Item Interactions for Compressing Recommendation Model Training Data","created":"2024-07-11","cited_by_count":1,"area":null}],"next_cursor":"100"}