| 1,107 |
AGL: A Scalable System for Industrial-purpose Graph Machine Learning |
2020 |
VLDB |
0.00012228654 |
| 1,109 |
Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks |
2022 |
VLDB |
0.00012227567 |
| 1,837 |
ByteGNN: Efficient Graph Neural Network Training at Large Scale |
2022 |
VLDB |
9.7435473e-05 |
| 2,595 |
Scalable and Efficient Full-Graph GNN Training for Large Graphs |
2023 |
SIGMOD |
8.4354632e-05 |
| 2,806 |
NeutronStar: Distributed GNN Training with Hybrid Dependency Management |
2022 |
SIGMOD |
8.1608806e-05 |
| 2,915 |
Ginex: SSD-enabled Billion-scale Graph Neural Network Training on a Single Machine via Provably Optimal In-memory Caching |
2022 |
VLDB |
8.0326719e-05 |
| 3,014 |
GraphScope: A Unified Engine For Big Graph Processing |
2021 |
VLDB |
7.9158581e-05 |
| 3,431 |
Saga: A Platform for Continuous Construction and Serving of Knowledge At Scale |
2022 |
SIGMOD |
7.4957047e-05 |
| 3,830 |
Scaling Attributed Network Embedding to Massive Graphs |
2021 |
VLDB |
7.1494819e-05 |
| 4,703 |
Incrementalizing Graph Algorithms |
2021 |
SIGMOD |
6.6177496e-05 |
| 4,800 |
NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams |
2024 |
VLDB |
6.5686201e-05 |
| 4,826 |
DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks |
2023 |
SIGMOD |
6.558209e-05 |
| 4,911 |
Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning |
2022 |
VLDB |
6.5128952e-05 |
| 5,133 |
ETC: Efficient Training of Temporal Graph Neural Networks over Large-scale Dynamic Graphs |
2024 |
VLDB |
6.416487e-05 |
| 5,258 |
Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective |
2024 |
VLDB |
6.3657895e-05 |
| 5,553 |
Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage Accesses |
2024 |
VLDB |
6.237297e-05 |
| 5,676 |
DynaHB: A Communication-Avoiding Asynchronous Distributed Framework with Hybrid Batches for Dynamic GNN Training |
2024 |
VLDB |
6.1906089e-05 |
| 6,168 |
EARLY: Efficient and Reliable Graph Neural Network for Dynamic Graphs |
2023 |
SIGMOD |
6.028641e-05 |
| 6,452 |
Reliable Data Distillation on Graph Convolutional Network |
2020 |
SIGMOD |
5.9318805e-05 |
| 6,490 |
Lotan: Bridging the Gap between GNNs and Scalable Graph Analytics Engines |
2023 |
VLDB |
5.9219237e-05 |
| 6,500 |
Play like a Vertex: A Stackelberg Game Approach for Streaming Graph Partitioning |
2024 |
SIGMOD |
5.9158694e-05 |
| 6,627 |
SIMPLE: Efficient Temporal Graph Neural Network Training at Scale with Dynamic Data Placement |
2024 |
SIGMOD |
5.8788666e-05 |
| 6,696 |
OUTRE: An OUT-of-core De-REdundancy GNN Training Framework for Massive Graphs within A Single Machine |
2024 |
VLDB |
5.8577737e-05 |
| 6,701 |
HongTu: Scalable Full-Graph GNN Training on Multiple GPUs |
2023 |
SIGMOD |
5.8566432e-05 |
| 6,974 |
DAHA: Accelerating GNN Training with Data and Hardware Aware Execution Planning |
2024 |
VLDB |
5.7953036e-05 |
| 7,139 |
Space-Efficient Random Walks on Streaming Graphs |
2023 |
VLDB |
5.7484571e-05 |
| 7,224 |
XGNN: Boosting Multi-GPU GNN Training via Global GNN Memory Store |
2024 |
VLDB |
5.7225443e-05 |
| 7,235 |
Extending Graph Patterns with Conditions |
2020 |
SIGMOD |
5.7195408e-05 |
| 7,243 |
SPG: Structure-Private Graph Database via SqueezePIR |
2023 |
VLDB |
5.7185134e-05 |
| 7,247 |
ADGNN: Towards Scalable GNN Training with Aggregation-Difference Aware Sampling |
2023 |
SIGMOD |
5.7178424e-05 |
| 7,464 |
GENTI: GPU-powered Walk-based Subgraph Extraction for Scalable Representation Learning on Dynamic Graphs |
2024 |
VLDB |
5.6733565e-05 |
| 7,466 |
Systems for Scalable Graph Analytics and Machine Learning: Trends and Methods |
2025 |
VLDB |
5.6731808e-05 |
| 7,682 |
GraphScope: A One-Stop Large Graph Processing System |
2021 |
VLDB |
5.6252285e-05 |
| 7,778 |
Distributed Graph Embedding with Information-Oriented Random Walks |
2023 |
VLDB |
5.6047813e-05 |
| 7,981 |
Capturing Associations in Graphs |
2020 |
VLDB |
5.5692809e-05 |
| 8,194 |
Deep Learning: Systems and Responsibility |
2021 |
SIGMOD |
5.5370263e-05 |
| 8,328 |
D3-GNN: Dynamic Distributed Dataflow for Streaming Graph Neural Networks |
2024 |
VLDB |
5.5065836e-05 |
| 9,366 |
Points-of-Interest Relationship Inference with Spatial-enriched Graph Neural Networks |
2022 |
VLDB |
5.339987e-05 |
| 9,395 |
NeutronTP: Load-Balanced Distributed Full-Graph GNN Training with Tensor Parallelism |
2025 |
VLDB |
5.3341661e-05 |
| 9,584 |
Scalable Graph Convolutional Network Training on Distributed-Memory Systems |
2023 |
VLDB |
5.3099516e-05 |
| 9,776 |
Capsule*: An Out-of-Core Training Mechanism for Colossal GNNs |
2025 |
SIGMOD |
5.2743647e-05 |
| 10,011 |
A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness |
2026 |
SIGMOD |
5.1725247e-05 |
| 10,027 |
NeutronHeter: Optimizing Distributed Graph Neural Network Training for Heterogeneous Clusters |
2026 |
SIGMOD |
5.1725247e-05 |
| 10,035 |
SWIFT: Enabling Large-Scale Temporal Graph Learning on a Single Machine |
2026 |
SIGMOD |
5.1725247e-05 |
| 10,226 |
Automated Tensor-Relational Decomposition for Large-Scale Sparse Tensor Computation |
2026 |
VLDB |
5.1725247e-05 |
| 10,233 |
Efficient GNN Training on Giant Graphs with Collective Batching and Scheduling |
2026 |
VLDB |
5.1725247e-05 |
| 10,310 |
NeutronCloud: Resource-Aware Distributed GNN Training in Fluctuating Cloud Environments |
2026 |
VLDB |
5.1725247e-05 |
| 10,334 |
Understanding Evolving Graph Structures for Large Discrete-Time Dynamic Graph Representation |
2026 |
VLDB |
5.1725247e-05 |
| 10,548 |
Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batch. |
2025 |
VLDB |
5.1725247e-05 |
| 10,579 |
NeutronTask: Scalable and Efficient Multi-GPU GNN Training with Task Parallelism |
2025 |
VLDB |
5.1725247e-05 |