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)
Incoming Non-self Citations Over Time
Authors
- 1. Gunduz Vehbi Demirci (Imagination Technologies)
- 2. Aparajita Haldar (University of Warwick)
- 3. Hakan Ferhatosmanoglu (Amazon; University of Warwick)
BibTeX Citation
@article{demirci_vldb23,
title = {{Scalable Graph Convolutional Network Training on Distributed-Memory Systems}},
author = {Demirci, Gunduz Vehbi and Haldar, Aparajita and Ferhatosmanoglu, Hakan},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {4},
pages = {711--724},
doi = {10.14778/3574245.3574256},
url = {https://doi.org/10.14778/3574245.3574256},
year = {2023}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,503 | Fight Fire with Fire: Towards Robust Graph Neural Networks on Dynamic Graphs via Actively Defense | 2024 | VLDB | 5.4132367e-05 |
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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.
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