Methods › General › Distributed Methods › DistDGL
DistDGL
Introduced by Da Zheng et al. in DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
DistDGL is a system for training GNNs in a mini-batch fashion on a cluster of machines. It is is based on the Deep Graph Library (DGL), a popular GNN development framework. DistDGL distributes the graph and its associated data (initial features and embeddings) across the machines and uses this distribution to derive a computational decomposition by following an owner-compute rule. DistDGL follows a synchronous training approach and allows ego-networks forming the mini-batches to include non-local nodes. To minimize the overheads associated with distributed computations, DistDGL uses a high-quality and light-weight mincut graph partitioning algorithm along with multiple balancing constraints. This allows it to reduce communication overheads and statically balance the computations. It further reduces the communication by replicating halo nodes and by using sparse embedding updates. The combination of these design choices allows DistDGL to train high-quality models while achieving high parallel efficiency and memory scalability
Papers archive 2025-07-28
7 shown of 7, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
-
MassiveGNN: Efficient Training via Prefetching for Massively Connected Distributed Graphs 30 Oct 2024 · 1 repository · arXiv:2410.22697
-
Entropy Aware Training for Fast and Accurate Distributed GNN 4 Nov 2023 · 1 repository · arXiv:2311.02399
-
BatchGNN: Efficient CPU-Based Distributed GNN Training on Very Large Graphs 23 Jun 2023 · 0 repositories · arXiv:2306.13814
-
Communication-Efficient Graph Neural Networks with Probabilistic Neighborhood Expansion Analysis and Caching 4 May 2023 · 2 repositories · arXiv:2305.03152
-
Distributed Graph Embedding with Information-Oriented Random Walks 28 Mar 2023 · 1 repository · arXiv:2303.15702
-
DistGNN-MB: Distributed Large-Scale Graph Neural Network Training on x86 via Minibatch Sampling 11 Nov 2022 · 0 repositories · arXiv:2211.06385
-
DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs 11 Oct 2020 · 1 repository · arXiv:2010.05337
Tasks archive 2025-07-28
9 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| graph partitioning | 3 |
| CPU | 2 |
| GPU | 2 |
| Graph Neural Network | 2 |
| Fraud Detection | 1 |
| Graph Embedding | 1 |
| Link Prediction | 1 |
| Recommendation Systems | 1 |
| Representation Learning | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections