| 1,162 |
Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks |
2022 |
VLDB |
0.00013573136 |
| 1,332 |
AGL: A Scalable System for Industrial-purpose Graph Machine Learning |
2020 |
VLDB |
0.00012549751 |
| 2,399 |
ByteGNN: Efficient Graph Neural Network Training at Large Scale |
2022 |
VLDB |
8.8869693e-05 |
| 3,028 |
NeutronStar: Distributed GNN Training with Hybrid Dependency Management |
2022 |
SIGMOD |
7.6833093e-05 |
| 3,092 |
Scalable and Efficient Full-Graph GNN Training for Large Graphs |
2023 |
SIGMOD |
7.5869574e-05 |
| 3,281 |
Ginex: SSD-enabled Billion-scale Graph Neural Network Training on a Single Machine via Provably Optimal In-memory Caching |
2022 |
VLDB |
7.2812392e-05 |
| 3,287 |
GraphScope: A Unified Engine For Big Graph Processing |
2021 |
VLDB |
7.2689944e-05 |
| 3,716 |
Saga: A Platform for Continuous Construction and Serving of Knowledge At Scale |
2022 |
SIGMOD |
6.8170433e-05 |
| 3,805 |
Scaling Attributed Network Embedding to Massive Graphs |
2021 |
VLDB |
6.7485579e-05 |
| 5,007 |
Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning |
2022 |
VLDB |
5.7581517e-05 |
| 5,016 |
DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks |
2023 |
SIGMOD |
5.7512359e-05 |
| 5,266 |
Incrementalizing Graph Algorithms |
2021 |
SIGMOD |
5.5949839e-05 |
| 5,356 |
NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams |
2024 |
VLDB |
5.5514335e-05 |
| 5,485 |
ETC: Efficient Training of Temporal Graph Neural Networks over Large-scale Dynamic Graphs |
2024 |
VLDB |
5.4817019e-05 |
| 5,570 |
Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage Accesses |
2024 |
VLDB |
5.4280174e-05 |
| 5,721 |
DynaHB: A Communication-Avoiding Asynchronous Distributed Framework with Hybrid Batches for Dynamic GNN Training |
2024 |
VLDB |
5.3538607e-05 |
| 5,746 |
Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective |
2024 |
VLDB |
5.3429324e-05 |
| 6,443 |
Play like a Vertex: A Stackelberg Game Approach for Streaming Graph Partitioning |
2024 |
SIGMOD |
5.0540558e-05 |
| 6,479 |
EARLY: Efficient and Reliable Graph Neural Network for Dynamic Graphs |
2023 |
SIGMOD |
5.0405101e-05 |
| 6,553 |
Reliable Data Distillation on Graph Convolutional Network |
2020 |
SIGMOD |
5.0100594e-05 |
| 6,885 |
Lotan: Bridging the Gap between GNNs and Scalable Graph Analytics Engines |
2023 |
VLDB |
4.8908367e-05 |
| 6,979 |
OUTRE: An OUT-of-core De-REdundancy GNN Training Framework for Massive Graphs within A Single Machine |
2024 |
VLDB |
4.8697538e-05 |
| 7,014 |
SIMPLE: Efficient Temporal Graph Neural Network Training at Scale with Dynamic Data Placement |
2024 |
SIGMOD |
4.8570865e-05 |
| 7,087 |
HongTu: Scalable Full-Graph GNN Training on Multiple GPUs |
2023 |
SIGMOD |
4.8324242e-05 |
| 7,212 |
Space-Efficient Random Walks on Streaming Graphs |
2023 |
VLDB |
4.7943898e-05 |
| 7,286 |
DAHA: Accelerating GNN Training with Data and Hardware Aware Execution Planning |
2024 |
VLDB |
4.7701372e-05 |
| 7,344 |
SPG: Structure-Private Graph Database via SqueezePIR |
2023 |
VLDB |
4.7508931e-05 |
| 7,543 |
XGNN: Boosting Multi-GPU GNN Training via Global GNN Memory Store |
2024 |
VLDB |
4.7103673e-05 |
| 7,566 |
Extending Graph Patterns with Conditions |
2020 |
SIGMOD |
4.7047078e-05 |
| 7,568 |
ADGNN: Towards Scalable GNN Training with Aggregation-Difference Aware Sampling |
2023 |
SIGMOD |
4.7044808e-05 |
| 7,609 |
Systems for Scalable Graph Analytics and Machine Learning: Trends and Methods |
2025 |
VLDB |
4.6921981e-05 |
| 7,750 |
GENTI: GPU-powered Walk-based Subgraph Extraction for Scalable Representation Learning on Dynamic Graphs |
2024 |
VLDB |
4.6565447e-05 |
| 7,819 |
GraphScope: A One-Stop Large Graph Processing System |
2021 |
VLDB |
4.6397234e-05 |
| 7,924 |
Distributed Graph Embedding with Information-Oriented Random Walks |
2023 |
VLDB |
4.6109814e-05 |
| 8,145 |
Capturing Associations in Graphs |
2020 |
VLDB |
4.5724134e-05 |
| 8,340 |
Deep Learning: Systems and Responsibility |
2021 |
SIGMOD |
4.5381546e-05 |
| 8,459 |
D3-GNN: Dynamic Distributed Dataflow for Streaming Graph Neural Networks |
2024 |
VLDB |
4.5008938e-05 |
| 9,359 |
Points-of-Interest Relationship Inference with Spatial-enriched Graph Neural Networks |
2022 |
VLDB |
4.3477383e-05 |
| 9,400 |
NeutronTP: Load-Balanced Distributed Full-Graph GNN Training with Tensor Parallelism |
2025 |
VLDB |
4.3399748e-05 |
| 9,596 |
Scalable Graph Convolutional Network Training on Distributed-Memory Systems |
2023 |
VLDB |
4.3150788e-05 |
| 9,785 |
Capsule*: An Out-of-Core Training Mechanism for Colossal GNNs |
2025 |
SIGMOD |
4.2799988e-05 |
| 10,011 |
A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,027 |
NeutronHeter: Optimizing Distributed Graph Neural Network Training for Heterogeneous Clusters |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,035 |
SWIFT: Enabling Large-Scale Temporal Graph Learning on a Single Machine |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,226 |
Automated Tensor-Relational Decomposition for Large-Scale Sparse Tensor Computation |
2026 |
VLDB |
4.1905499e-05 |
| 10,233 |
Efficient GNN Training on Giant Graphs with Collective Batching and Scheduling |
2026 |
VLDB |
4.1905499e-05 |
| 10,310 |
NeutronCloud: Resource-Aware Distributed GNN Training in Fluctuating Cloud Environments |
2026 |
VLDB |
4.1905499e-05 |
| 10,334 |
Understanding Evolving Graph Structures for Large Discrete-Time Dynamic Graph Representation |
2026 |
VLDB |
4.1905499e-05 |
| 10,548 |
Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batch. |
2025 |
VLDB |
4.1905499e-05 |
| 10,579 |
NeutronTask: Scalable and Efficient Multi-GPU GNN Training with Task Parallelism |
2025 |
VLDB |
4.1905499e-05 |