{"total":42,"rows":[{"id":"2106.06935","title":"Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction","created":"2021-06-13","cited_by_count":275,"area":null},{"id":"2207.04606","title":"SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning","created":"2022-07-11","cited_by_count":47,"area":null},{"id":"2211.00216","title":"Distributed Graph Neural Network Training: A Survey","created":"2022-11-01","cited_by_count":32,"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":"2110.09524","title":"Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective","created":"2021-10-18","cited_by_count":28,"area":null},{"id":"2308.11825","title":"Accel-GCN: High-Performance GPU Accelerator Design for Graph Convolution Networks","created":"2023-08-22","cited_by_count":22,"area":null},{"id":"2112.02052","title":"TC-GNN: Bridging Sparse GNN Computation and Dense Tensor Cores on GPUs","created":"2021-12-03","cited_by_count":17,"area":null},{"id":"2212.11408","title":"Adaptive and Dynamic Multi-Resolution Hashing for Pairwise Summations","created":"2022-12-21","cited_by_count":14,"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":"2210.10737","title":"RSC: Accelerating Graph Neural Networks Training via Randomized Sparse Computations","created":"2022-10-19","cited_by_count":12,"area":null},{"id":"2203.00158","title":"GROW: A Row-Stationary Sparse-Dense GEMM Accelerator for Memory-Efficient Graph Convolutional Neural Networks","created":"2022-03-01","cited_by_count":5,"area":null},{"id":"2308.12093","title":"Cached Operator Reordering: A Unified View for Fast GNN Training","created":"2023-08-23","cited_by_count":5,"area":null},{"id":"2301.00391","title":"PiPAD: Pipelined and Parallel Dynamic GNN Training on GPUs","created":"2023-01-01","cited_by_count":4,"area":null},{"id":"2305.02522","title":"BitGNN: Unleashing the Performance Potential of Binary Graph Neural Networks on GPUs","created":"2023-05-04","cited_by_count":4,"area":null},{"id":"2308.00890","title":"Tango: rethinking quantization for graph neural network training on GPUs","created":"2023-08-02","cited_by_count":4,"area":null},{"id":"2303.16999","title":"PopSparse: Accelerated block sparse matrix multiplication on IPU","created":"2023-03-29","cited_by_count":3,"area":null},{"id":"2404.04118","title":"GNNBENCH: Fair and Productive Benchmarking for Single-GPU GNN System","created":"2024-04-05","cited_by_count":3,"area":null},{"id":"2412.11007","title":"FlashSparse: Minimizing Computation Redundancy for Fast Sparse Matrix Multiplications on Tensor Cores","created":"2024-12-15","cited_by_count":3,"area":null},{"id":"2104.10716","title":"Accelerating SpMM Kernel with Cache-First Edge Sampling for Graph Neural Networks","created":"2021-04-21","cited_by_count":2,"area":null},{"id":"2203.06944","title":"Towards Neural Sparse Linear Solvers","created":"2022-03-14","cited_by_count":2,"area":null},{"id":"2212.05009","title":"Scalable Graph Convolutional Network Training on Distributed-Memory Systems","created":"2022-12-09","cited_by_count":2,"area":null},{"id":"2504.13266","title":"Graph Learning at Scale: Characterizing and Optimizing Pre-Propagation GNNs","created":"2025-04-17","cited_by_count":2,"area":null},{"id":"2207.09955","title":"Operation-Level Performance Benchmarking of Graph Neural Networks for Scientific Applications","created":"2022-07-20","cited_by_count":1,"area":null},{"id":"2304.07613","title":"STen: Productive and Efficient Sparsity in PyTorch","created":"2023-04-15","cited_by_count":1,"area":null},{"id":"2305.17408","title":"AdaptGear: Accelerating GNN Training via Adaptive Subgraph-Level Kernels on GPUs","created":"2023-05-27","cited_by_count":1,"area":null},{"id":"2402.03548","title":"Single-GPU GNN Systems: Traps and Pitfalls","created":"2024-02-05","cited_by_count":1,"area":null},{"id":"2411.01109","title":"Optimization of GNN Training Through Half-precision","created":"2024-11-02","cited_by_count":1,"area":null},{"id":"2412.12218","title":"Accelerating Sparse Graph Neural Networks with Tensor Core Optimization","created":"2024-12-16","cited_by_count":1,"area":null},{"id":"2506.22714","title":"Libra: Unleashing GPU Heterogeneity for High-Performance Sparse Matrix Multiplication","created":"2025-06-28","cited_by_count":1,"area":null},{"id":"2111.07684","title":"AutoGMap: Learning to Map Large-scale Sparse Graphs on Memristive Crossbars","created":"2021-11-15","cited_by_count":0,"area":null},{"id":"2212.00827","title":"Architectural Implications of Embedding Dimension during GCN on CPU and GPU","created":"2022-12-01","cited_by_count":0,"area":null},{"id":"2309.01030","title":"Online Adaptive Mahalanobis Distance Estimation","created":"2023-09-02","cited_by_count":0,"area":null},{"id":"2404.03019","title":"GeoT: Tensor Centric Library for Graph Neural Network via Efficient Segment Reduction on GPU","created":"2024-04-03","cited_by_count":0,"area":null},{"id":"2508.04077","title":"The Ubiquitous Sparse Matrix-Matrix Products","created":"2025-08-06","cited_by_count":0,"area":null},{"id":"2511.01654","title":"Panther: A Cost-Effective Privacy-Preserving Framework for GNN Training and Inference Services in Cloud Environments","created":"2025-11-03","cited_by_count":0,"area":null},{"id":"2511.17594","title":"AutoSAGE: Input-Aware CUDA Scheduling for Sparse GNN Aggregation (SpMM/SDDMM) and CSR Attention","created":"2025-11-17","cited_by_count":0,"area":null},{"id":"2602.13140","title":"FlashSchNet: Fast and Accurate Coarse-Grained Neural Network Molecular Dynamics","created":"2026-02-13","cited_by_count":0,"area":null},{"id":"2605.31500","title":"On Efficient Scaling of GNNs via IO-Aware Layers Implementations","created":"2026-05-29","cited_by_count":0,"area":null},{"id":"2607.07422","title":"InductWave: Inductive Multi-Hop Logical Query Answering on Knowledge Graphs","created":"2026-07-08","cited_by_count":0,"area":null},{"id":"2607.11952","title":"Scalable Optimal Transport Algorithm for Network Alignment","created":"2026-07-11","cited_by_count":0,"area":null},{"id":"2608.29362","title":"Spectral Analysis for Sparse Matrix Computation: Insights and Potential","created":"2026-08-29","cited_by_count":0,"area":null},{"id":"2609.34499","title":"Scalable GNN-based Knowledge Graph Representation Learning with Efficient Message Passing","created":"2026-09-28","cited_by_count":0,"area":null}],"next_cursor":null}