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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
- 13325
- Venue
- VLDB
- Year
- 2023
- Pagerank
- 4.3150788e-05
- Overall Rank
- 9,596 | 33.31%
- 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 |
0.0019040811 |
| 271 |
AliGraph: A Comprehensive Graph Neural Network Platform |
2019 |
VLDB |
0.00029565193 |
| 395 |
One Trillion Edges: Graph Processing at Facebook-Scale |
2015 |
VLDB |
0.00024440144 |
| 558 |
Trinity: A Distributed Graph Engine on a Memory Cloud |
2013 |
SIGMOD |
0.00020158056 |
| 570 |
From "Think Like a Vertex" to "Think Like a Graph" |
2014 |
VLDB |
0.00019895021 |
| 1,102 |
Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture |
2021 |
VLDB |
0.00014011556 |
| 1,162 |
Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks |
2022 |
VLDB |
0.00013573136 |
| 1,170 |
Blogel: A Block-Centric Framework for Distributed Computation on Real-World Graphs |
2014 |
VLDB |
0.00013526297 |
| 1,332 |
AGL: A Scalable System for Industrial-purpose Graph Machine Learning |
2020 |
VLDB |
0.00012549751 |
| 1,388 |
TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs |
2022 |
VLDB |
0.00012249747 |
| 1,410 |
An Experimental Comparison of Pregel-like Graph Processing Systems |
2014 |
VLDB |
0.00012127229 |
| 1,876 |
Large-Scale Distributed Graph Computing Systems: An Experimental Evaluation |
2015 |
VLDB |
0.00010242818 |
| 1,977 |
Towards Effective Partition Management for Large Graphs |
2012 |
SIGMOD |
9.8780062e-05 |
| 2,165 |
Accelerating Large Scale Real-Time GNN Inference using Channel Pruning |
2021 |
VLDB |
9.3925908e-05 |
| 2,399 |
ByteGNN: Efficient Graph Neural Network Training at Large Scale |
2022 |
VLDB |
8.8869693e-05 |
| 2,672 |
Cumulon: Optimizing Statistical Data Analysis in the Cloud |
2013 |
SIGMOD |
8.3334428e-05 |
| 2,678 |
HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework |
2022 |
VLDB |
8.3224016e-05 |
| 3,287 |
GraphScope: A Unified Engine For Big Graph Processing |
2021 |
VLDB |
7.2689944e-05 |
| 3,864 |
A Partition-Based Approach to Structure Similarity Search |
2014 |
VLDB |
6.6813849e-05 |
| 3,988 |
G3: When Graph Neural Networks Meet Parallel Graph Processing Systems on GPUs |
2020 |
VLDB |
6.5548465e-05 |
| 4,601 |
Distributed Deep Learning on Data Systems: A Comparative Analysis of Approaches |
2021 |
VLDB |
6.05274e-05 |
| 4,868 |
Application Driven Graph Partitioning |
2020 |
SIGMOD |
5.8595544e-05 |
| 4,955 |
Horton+: A Distributed System for Processing Declarative Reachability Queries over Partitioned Graphs |
2013 |
VLDB |
5.8057282e-05 |
| 5,014 |
TurboGraph++: A Scalable and Fast Graph Analytics System |
2018 |
SIGMOD |
5.7519428e-05 |
| 5,383 |
Parallel Training of Knowledge Graph Embedding Models: A Comparison of Techniques |
2022 |
VLDB |
5.5357645e-05 |
| 8,250 |
A Study of Partitioning Policies for Graph Analytics on Large-scale Distributed Platforms |
2019 |
VLDB |
4.5448177e-05 |
| 8,864 |
Cerebro: A Layered Data Platform for Scalable Deep Learning |
2021 |
CIDR |
4.4283952e-05 |
| 9,174 |
MemFlow: Memory-Aware Distributed Deep Learning |
2020 |
SIGMOD |
4.3807157e-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 |
4.1905499e-05 |
| 8,459 |
D3-GNN: Dynamic Distributed Dataflow for Streaming Graph Neural Networks |
2024 |
VLDB |
4.5008938e-05 |
| 5,746 |
Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective |
2024 |
VLDB |
5.3429324e-05 |
| 1,876 |
Large-Scale Distributed Graph Computing Systems: An Experimental Evaluation |
2015 |
VLDB |
0.00010242818 |
| 5,016 |
DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks |
2023 |
SIGMOD |
5.7512359e-05 |
| 10,664 |
Effective and Efficient Distributed Temporal Graph Learning through Hotspot Memory Sharing |
2025 |
VLDB |
4.1905499e-05 |
| 9,176 |
GraphGem: Optimized Scalable System for Graph Convolutional Networks |
2021 |
SIGMOD |
4.380382e-05 |
| 2,399 |
ByteGNN: Efficient Graph Neural Network Training at Large Scale |
2022 |
VLDB |
8.8869693e-05 |
| 3,092 |
Scalable and Efficient Full-Graph GNN Training for Large Graphs |
2023 |
SIGMOD |
7.5869574e-05 |
| 1,102 |
Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecture |
2021 |
VLDB |
0.00014011556 |