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Scalable Graph Convolutional Network Training on Distributed-Memory Systems
Summary: Distributed-memory GCN training with vertex-wise partitioning and non-blocking point-to-point communication, scaling to many processors, deeper models, and billion-node graphs. Uses hypergraph partitioning and a stochastic mini-batch hypergraph model to accurately encode and minimize communication, outperforming standard graph partitioning.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 13324
- Venue
- VLDB
- Year
- 2023
- Pagerank
- 4.319218e-05
- Overall Rank
- 9,596 | 33.25%
- DOI
-
10.14778/3574245.3574256
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 28 of 28 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 4 |
Pregel: A System for Large-Scale Graph Processing |
2010 |
SIGMOD |
0.0019005923 |
| 278 |
AliGraph: A Comprehensive Graph Neural Network Platform |
2019 |
VLDB |
0.00029230623 |
| 396 |
One Trillion Edges: Graph Processing at Facebook-Scale |
2015 |
VLDB |
0.00024424102 |
| 558 |
Trinity: A Distributed Graph Engine on a Memory Cloud |
2013 |
SIGMOD |
0.00020168032 |
| 574 |
From "Think Like a Vertex" to "Think Like a Graph" |
2014 |
VLDB |
0.00019883211 |
| 1,103 |
Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture |
2021 |
VLDB |
0.00014025101 |
| 1,160 |
Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks |
2022 |
VLDB |
0.00013586221 |
| 1,171 |
Blogel: A Block-Centric Framework for Distributed Computation on Real-World Graphs |
2014 |
VLDB |
0.00013511313 |
| 1,329 |
AGL: A Scalable System for Industrial-purpose Graph Machine Learning |
2020 |
VLDB |
0.00012561848 |
| 1,387 |
TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs |
2022 |
VLDB |
0.00012261568 |
| 1,408 |
An Experimental Comparison of Pregel-like Graph Processing Systems |
2014 |
VLDB |
0.00012133511 |
| 1,877 |
Large-Scale Distributed Graph Computing Systems: An Experimental Evaluation |
2015 |
VLDB |
0.00010236803 |
| 1,976 |
Towards Effective Partition Management for Large Graphs |
2012 |
SIGMOD |
9.8844201e-05 |
| 2,177 |
Accelerating Large Scale Real-Time GNN Inference using Channel Pruning |
2021 |
VLDB |
9.359876e-05 |
| 2,400 |
ByteGNN: Efficient Graph Neural Network Training at Large Scale |
2022 |
VLDB |
8.8955105e-05 |
| 2,667 |
Cumulon: Optimizing Statistical Data Analysis in the Cloud |
2013 |
SIGMOD |
8.3413995e-05 |
| 2,677 |
HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework |
2022 |
VLDB |
8.3268401e-05 |
| 3,287 |
GraphScope: A Unified Engine For Big Graph Processing |
2021 |
VLDB |
7.2739447e-05 |
| 3,862 |
A Partition-Based Approach to Structure Similarity Search |
2014 |
VLDB |
6.687769e-05 |
| 3,986 |
G3: When Graph Neural Networks Meet Parallel Graph Processing Systems on GPUs |
2020 |
VLDB |
6.5611714e-05 |
| 4,557 |
Distributed Deep Learning on Data Systems: A Comparative Analysis of Approaches |
2021 |
VLDB |
6.087611e-05 |
| 4,867 |
Application Driven Graph Partitioning |
2020 |
SIGMOD |
5.8651797e-05 |
| 4,949 |
Horton+: A Distributed System for Processing Declarative Reachability Queries over Partitioned Graphs |
2013 |
VLDB |
5.8113132e-05 |
| 5,017 |
TurboGraph++: A Scalable and Fast Graph Analytics System |
2018 |
SIGMOD |
5.7574792e-05 |
| 5,377 |
Parallel Training of Knowledge Graph Embedding Models: A Comparison of Techniques |
2022 |
VLDB |
5.5410858e-05 |
| 8,254 |
A Study of Partitioning Policies for Graph Analytics on Large-scale Distributed Platforms |
2019 |
VLDB |
4.5491792e-05 |
| 8,864 |
Cerebro: A Layered Data Platform for Scalable Deep Learning |
2021 |
CIDR |
4.4326439e-05 |
| 9,170 |
MemFlow: Memory-Aware Distributed Deep Learning |
2020 |
SIGMOD |
4.3849075e-05 |
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| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 10,233 |
Efficient GNN Training on Giant Graphs with Collective Batching and Scheduling |
2026 |
VLDB |
4.1945683e-05 |
| 8,463 |
D3-GNN: Dynamic Distributed Dataflow for Streaming Graph Neural Networks |
2024 |
VLDB |
4.5052127e-05 |
| 5,737 |
Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective |
2024 |
VLDB |
5.3480667e-05 |
| 1,877 |
Large-Scale Distributed Graph Computing Systems: An Experimental Evaluation |
2015 |
VLDB |
0.00010236803 |
| 5,018 |
DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks |
2023 |
SIGMOD |
5.7567672e-05 |
| 10,656 |
Effective and Efficient Distributed Temporal Graph Learning through Hotspot Memory Sharing |
2025 |
VLDB |
4.1945683e-05 |
| 9,172 |
GraphGem: Optimized Scalable System for Graph Convolutional Networks |
2021 |
SIGMOD |
4.3845844e-05 |
| 2,400 |
ByteGNN: Efficient Graph Neural Network Training at Large Scale |
2022 |
VLDB |
8.8955105e-05 |
| 3,087 |
Scalable and Efficient Full-Graph GNN Training for Large Graphs |
2023 |
SIGMOD |
7.5939896e-05 |
| 1,103 |
Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture |
2021 |
VLDB |
0.00014025101 |