{"list":"other","total":62,"rows":[{"id":"1609.02907","title":"Semi-Supervised Classification with Graph Convolutional Networks","created":"2016-09-09","cited_by_count":11361},{"id":"1710.10903","title":"Graph Attention Networks","created":"2017-10-30","cited_by_count":8923},{"id":"1706.02216","title":"Inductive Representation Learning on Large Graphs","created":"2017-06-07","cited_by_count":6320},{"id":"1810.00826","title":"How Powerful are Graph Neural Networks?","created":"2018-10-01","cited_by_count":4393},{"id":"1902.07153","title":"Simplifying Graph Convolutional Networks","created":"2019-02-19","cited_by_count":1477},{"id":"1603.08861","title":"Revisiting Semi-Supervised Learning with Graph Embeddings","created":"2016-03-29","cited_by_count":976},{"id":"2007.08663","title":"TUDataset: A collection of benchmark datasets for learning with graphs","created":"2020-07-16","cited_by_count":728},{"id":"2003.00982","title":"Benchmarking Graph Neural Networks","created":"2020-03-02","cited_by_count":680},{"id":"1907.04931","title":"GraphSAINT: Graph Sampling Based Inductive Learning Method","created":"2019-07-10","cited_by_count":618},{"id":"1801.10247","title":"FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling","created":"2018-01-30","cited_by_count":584},{"id":"1905.07953","title":"Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks","created":"2019-05-20","cited_by_count":541},{"id":"1910.09700","title":"Quantifying the Carbon Emissions of Machine Learning","created":"2019-10-21","cited_by_count":521},{"id":"2203.11932","title":"Dataset Distillation by Matching Training Trajectories","created":"2022-03-22","cited_by_count":442},{"id":"2103.09430","title":"OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs","created":"2021-03-17","cited_by_count":329},{"id":"1903.11680","title":"Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks","created":"2019-03-27","cited_by_count":159},{"id":"2110.07580","title":"Graph Condensation for Graph Neural Networks","created":"2021-10-14","cited_by_count":150},{"id":"2206.07746","title":"Condensing Graphs via One-Step Gradient Matching","created":"2022-06-15","cited_by_count":100},{"id":"2311.15529","title":"Efficient Dataset Distillation via Minimax Diffusion","created":"2023-11-27","cited_by_count":83},{"id":"2407.15138","title":"D$^4$M: Dataset Distillation via Disentangled Diffusion Model","created":"2024-07-21","cited_by_count":77},{"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},{"id":"2001.08184","title":"GraphGen: A Scalable Approach to Domain-agnostic Labeled Graph Generation","created":"2020-01-22","cited_by_count":66},{"id":"2206.13697","title":"Graph Condensation via Receptive Field Distribution Matching","created":"2022-06-28","cited_by_count":60},{"id":"2402.03358","title":"A Comprehensive Survey on Graph Reduction: Sparsification, Coarsening, and Condensation","created":"2024-01-29","cited_by_count":57},{"id":"2011.07682","title":"A Large-Scale Database for Graph Representation Learning","created":"2020-11-16","cited_by_count":55},{"id":"2309.09455","title":"CaT: Balanced Continual Graph Learning with Graph Condensation","created":"2023-09-18","cited_by_count":52},{"id":"2410.12771","title":"Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models","created":"2024-10-16","cited_by_count":52},{"id":"2310.09192","title":"Does Graph Distillation See Like Vision Dataset Counterpart?","created":"2023-10-13","cited_by_count":45},{"id":"2210.01906","title":"Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks","created":"2022-10-04","cited_by_count":41},{"id":"2310.11046","title":"Fast Graph Condensation with Structure-based Neural Tangent Kernel","created":"2023-10-17","cited_by_count":36},{"id":"2307.15967","title":"Graph Condensation for Inductive Node Representation Learning","created":"2023-07-29","cited_by_count":31},{"id":"2402.05011","title":"Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching","created":"2024-02-07","cited_by_count":28},{"id":"2401.11720","title":"Graph Condensation: A Survey","created":"2024-01-22","cited_by_count":23},{"id":"2402.05962","title":"EXGC: Bridging Efficiency and Explainability in Graph Condensation","created":"2024-02-05","cited_by_count":20},{"id":"2403.07294","title":"Graph Data Condensation via Self-expressive Graph Structure Reconstruction","created":"2024-03-12","cited_by_count":18},{"id":"1803.06272","title":"Graph Partition Neural Networks for Semi-Supervised Classification","created":"2018-03-16","cited_by_count":17},{"id":"2310.09202","title":"Graph Distillation with Eigenbasis Matching","created":"2023-10-13","cited_by_count":16},{"id":"2008.09657","title":"GraphReach: Position-Aware Graph Neural Network using Reachability Estimations","created":"2020-08-19","cited_by_count":14},{"id":"2211.15335","title":"You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets","created":"2022-11-28","cited_by_count":14},{"id":"2306.08277","title":"FRIGATE: Frugal Spatio-temporal Forecasting on Road Networks","created":"2023-06-14","cited_by_count":14},{"id":"2406.15575","title":"Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity","created":"2024-06-21","cited_by_count":14},{"id":"2310.09486","title":"Mirage: Model-Agnostic Graph Distillation for Graph Classification","created":"2023-10-14","cited_by_count":13},{"id":"2407.00615","title":"GC-Bench: An Open and Unified Benchmark for Graph Condensation","created":"2024-06-30","cited_by_count":12},{"id":"2401.12231","title":"Disentangled Condensation for Large-scale Graphs","created":"2024-01-18","cited_by_count":10},{"id":"2311.15772","title":"Attend Who is Weak: Enhancing Graph Condensation via Cross-Free Adversarial Training","created":"2023-11-27","cited_by_count":9},{"id":"2312.14439","title":"PUMA: Efficient Continual Graph Learning for Node Classification with Graph Condensation","created":"2023-12-22","cited_by_count":9},{"id":"2402.02000","title":"A Survey on Graph Condensation","created":"2024-02-03","cited_by_count":9},{"id":"2405.17003","title":"Graph Condensation for Open-World Graph Learning","created":"2024-05-27","cited_by_count":9},{"id":"2402.04924","title":"Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient Matching","created":"2024-02-07","cited_by_count":8},{"id":"2405.03911","title":"Federated Graph Condensation with Information Bottleneck Principles","created":"2024-05-07","cited_by_count":8},{"id":"2306.03447","title":"GRAFENNE: Learning on Graphs with Heterogeneous and Dynamic Feature Sets","created":"2023-06-06","cited_by_count":7},{"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},{"id":"2108.11571","title":"GNNSampler: Bridging the Gap between Sampling Algorithms of GNN and Hardware","created":"2021-08-26","cited_by_count":6},{"id":"2411.17063","title":"Contrastive Graph Condensation: Advancing Data Versatility through Self-Supervised Learning","created":"2024-11-26","cited_by_count":6},{"id":"2505.02573","title":"Rethinking Federated Graph Learning: A Data Condensation Perspective","created":"2025-05-05","cited_by_count":6},{"id":"2301.12477","title":"StriderNET: A Graph Reinforcement Learning Approach to Optimize Atomic Structures on Rough Energy Landscapes","created":"2023-01-29","cited_by_count":4},{"id":"2412.17355","title":"Bi-Directional Multi-Scale Graph Dataset Condensation via Information Bottleneck","created":"2024-12-23","cited_by_count":4},{"id":"2501.02565","title":"Efficient Graph Condensation via Gaussian Process","created":"2025-01-05","cited_by_count":4},{"id":"2505.20807","title":"Simple yet Effective Graph Distillation via Clustering","created":"2025-05-27","cited_by_count":4},{"id":"2203.07969","title":"PDNS-Net: A Large Heterogeneous Graph Benchmark Dataset of Network Resolutions for Graph Learning","created":"2022-03-15","cited_by_count":3},{"id":"2412.06173","title":"Revisiting the Necessity of Graph Learning and Common Graph Benchmarks","created":"2024-12-09","cited_by_count":3},{"id":"2505.18358","title":"CONCORD: Concept-Informed Diffusion for Dataset Distillation","created":"2025-05-23","cited_by_count":3},{"id":"2408.16871","title":"GSTAM: Efficient Graph Distillation with Structural Attention-Matching","created":"2024-08-29","cited_by_count":1}],"next_cursor":null}