| 1,160 |
Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks |
2022 |
VLDB |
0.00013586221 |
| 1,329 |
AGL: A Scalable System for Industrial-purpose Graph Machine Learning |
2020 |
VLDB |
0.00012561848 |
| 2,400 |
ByteGNN: Efficient Graph Neural Network Training at Large Scale |
2022 |
VLDB |
8.8955105e-05 |
| 3,025 |
NeutronStar: Distributed GNN Training with Hybrid Dependency Management |
2022 |
SIGMOD |
7.6906935e-05 |
| 3,087 |
Scalable and Efficient Full-Graph GNN Training for Large Graphs |
2023 |
SIGMOD |
7.5939896e-05 |
| 3,276 |
Ginex: SSD-enabled Billion-scale Graph Neural Network Training on a Single Machine via Provably Optimal In-memory Caching |
2022 |
VLDB |
7.2879718e-05 |
| 3,287 |
GraphScope: A Unified Engine For Big Graph Processing |
2021 |
VLDB |
7.2739447e-05 |
| 3,711 |
Saga: A Platform for Continuous Construction and Serving of Knowledge At Scale |
2022 |
SIGMOD |
6.823609e-05 |
| 3,803 |
Scaling Attributed Network Embedding to Massive Graphs |
2021 |
VLDB |
6.7550628e-05 |
| 5,007 |
Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning |
2022 |
VLDB |
5.763689e-05 |
| 5,018 |
DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks |
2023 |
SIGMOD |
5.7567672e-05 |
| 5,292 |
Incrementalizing Graph Algorithms |
2021 |
SIGMOD |
5.5816687e-05 |
| 5,345 |
NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams |
2024 |
VLDB |
5.5567697e-05 |
| 5,475 |
ETC: Efficient Training of Temporal Graph Neural Networks over Large-scale Dynamic Graphs |
2024 |
VLDB |
5.4869706e-05 |
| 5,561 |
Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage Accesses |
2024 |
VLDB |
5.4332062e-05 |
| 5,710 |
DynaHB: A Communication-Avoiding Asynchronous Distributed Framework with Hybrid Batches for Dynamic GNN Training |
2024 |
VLDB |
5.3590055e-05 |
| 5,737 |
Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective |
2024 |
VLDB |
5.3480667e-05 |
| 6,446 |
Play like a Vertex: A Stackelberg Game Approach for Streaming Graph Partitioning |
2024 |
SIGMOD |
5.0588808e-05 |
| 6,485 |
EARLY: Efficient and Reliable Graph Neural Network for Dynamic Graphs |
2023 |
SIGMOD |
5.0453531e-05 |
| 6,566 |
Reliable Data Distillation on Graph Convolutional Network |
2020 |
SIGMOD |
5.0074274e-05 |
| 6,884 |
Lotan: Bridging the Gap between GNNs and Scalable Graph Analytics Engines |
2023 |
VLDB |
4.8955332e-05 |
| 6,980 |
OUTRE: An OUT-of-core De-REdundancy GNN Training Framework for Massive Graphs within A Single Machine |
2024 |
VLDB |
4.8744298e-05 |
| 7,014 |
SIMPLE: Efficient Temporal Graph Neural Network Training at Scale with Dynamic Data Placement |
2024 |
SIGMOD |
4.8616315e-05 |
| 7,091 |
HongTu: Scalable Full-Graph GNN Training on Multiple GPUs |
2023 |
SIGMOD |
4.8370645e-05 |
| 7,212 |
Space-Efficient Random Walks on Streaming Graphs |
2023 |
VLDB |
4.7989929e-05 |
| 7,289 |
DAHA: Accelerating GNN Training with Data and Hardware Aware Execution Planning |
2024 |
VLDB |
4.7747168e-05 |
| 7,347 |
SPG: Structure-Private Graph Database via SqueezePIR |
2023 |
VLDB |
4.7554541e-05 |
| 7,545 |
XGNN: Boosting Multi-GPU GNN Training via Global GNN Memory Store |
2024 |
VLDB |
4.714889e-05 |
| 7,566 |
ADGNN: Towards Scalable GNN Training with Aggregation-Difference Aware Sampling |
2023 |
SIGMOD |
4.7089968e-05 |
| 7,607 |
Systems for Scalable Graph Analytics and Machine Learning: Trends and Methods |
2025 |
VLDB |
4.6967024e-05 |
| 7,641 |
Extending Graph Patterns with Conditions |
2020 |
SIGMOD |
4.6902655e-05 |
| 7,749 |
GENTI: GPU-powered Walk-based Subgraph Extraction for Scalable Representation Learning on Dynamic Graphs |
2024 |
VLDB |
4.6610143e-05 |
| 7,813 |
GraphScope: A One-Stop Large Graph Processing System |
2021 |
VLDB |
4.6441779e-05 |
| 7,924 |
Distributed Graph Embedding with Information-Oriented Random Walks |
2023 |
VLDB |
4.6154072e-05 |
| 8,211 |
Capturing Associations in Graphs |
2020 |
VLDB |
4.5581054e-05 |
| 8,346 |
Deep Learning: Systems and Responsibility |
2021 |
SIGMOD |
4.5420668e-05 |
| 8,463 |
D3-GNN: Dynamic Distributed Dataflow for Streaming Graph Neural Networks |
2024 |
VLDB |
4.5052127e-05 |
| 9,353 |
Points-of-Interest Relationship Inference with Spatial-enriched Graph Neural Networks |
2022 |
VLDB |
4.3519095e-05 |
| 9,395 |
NeutronTP: Load-Balanced Distributed Full-Graph GNN Training with Tensor Parallelism |
2025 |
VLDB |
4.3441378e-05 |
| 9,596 |
Scalable Graph Convolutional Network Training on Distributed-Memory Systems |
2023 |
VLDB |
4.319218e-05 |
| 9,804 |
Capsule*: An Out-of-Core Training Mechanism for Colossal GNNs |
2025 |
SIGMOD |
4.2805224e-05 |
| 10,011 |
A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,027 |
NeutronHeter: Optimizing Distributed Graph Neural Network Training for Heterogeneous Clusters |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,035 |
SWIFT: Enabling Large-Scale Temporal Graph Learning on a Single Machine |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,226 |
Automated Tensor-Relational Decomposition for Large-Scale Sparse Tensor Computation |
2026 |
VLDB |
4.1945683e-05 |
| 10,233 |
Efficient GNN Training on Giant Graphs with Collective Batching and Scheduling |
2026 |
VLDB |
4.1945683e-05 |
| 10,298 |
NeutronCloud: Resource-Aware Distributed GNN Training in Fluctuating Cloud Environments |
2026 |
VLDB |
4.1945683e-05 |
| 10,322 |
Understanding Evolving Graph Structures for Large Discrete-Time Dynamic Graph Representation |
2026 |
VLDB |
4.1945683e-05 |
| 10,539 |
Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batch. |
2025 |
VLDB |
4.1945683e-05 |
| 10,570 |
NeutronTask: Scalable and Efficient Multi-GPU GNN Training with Task Parallelism |
2025 |
VLDB |
4.1945683e-05 |