Back to papers
D3-GNN: Dynamic Distributed Dataflow for Streaming Graph Neural Networks
Summary: D3-GNN: distributed hybrid-parallel streaming GNN that unrolls a distributed computation graph to maintain dynamic node embeddings online with fault-tolerance, low latency, and balanced load. Introduces inter-/intra-layer windowed forward-pass to curb skew and neighbor explosion; shows ~76× throughput vs DGL and further ~10× runtime and up to 15× message-volume reduction at high parallelism.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 13497
- Venue
- VLDB
- Year
- 2024
- Pagerank
- 4.5052127e-05
- Overall Rank
- 8,463 | 41.13%
- DOI
-
10.14778/3681954.3681961
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 13 of 13 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 278 |
AliGraph: A Comprehensive Graph Neural Network Platform |
2019 |
VLDB |
0.00029230623 |
| 331 |
The Ubiquity of Large Graphs and Surprising Challenges of Graph Processing |
2018 |
VLDB |
0.00027214222 |
| 1,160 |
Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks |
2022 |
VLDB |
0.00013586221 |
| 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 |
| 1,394 |
Real-time Constrained Cycle Detection in Large Dynamic Graphs |
2018 |
VLDB |
0.0001221552 |
| 2,494 |
Streaming Graph Partitioning: An Experimental Study |
2018 |
VLDB |
8.6508229e-05 |
| 2,826 |
Regular Path Query Evaluation on Streaming Graphs |
2020 |
SIGMOD |
8.056119e-05 |
| 3,232 |
Managing Large Dynamic Graphs Efficiently |
2012 |
SIGMOD |
7.336861e-05 |
| 5,211 |
Tornado: A System For Real-Time Iterative Analysis Over Evolving Data |
2016 |
SIGMOD |
5.6284829e-05 |
| 6,528 |
StreamWorks - A system for Dynamic Graph Search |
2013 |
SIGMOD |
5.0253074e-05 |
| 6,721 |
Beyond Analytics: The Evolution of Stream Processing Systems |
2020 |
SIGMOD |
4.9492015e-05 |
| 9,264 |
Model-Parallel Model Selection for Deep Learning Systems |
2021 |
SIGMOD |
4.3675421e-05 |
Semantically Similar Papers
| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 7,566 |
ADGNN: Towards Scalable GNN Training with Aggregation-Difference Aware Sampling |
2023 |
SIGMOD |
4.7089968e-05 |
| 6,942 |
Efficient Training of Graph Neural Networks on Large Graphs |
2024 |
VLDB |
4.8922884e-05 |
| 5,345 |
NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams |
2024 |
VLDB |
5.5567697e-05 |
| 2,400 |
ByteGNN: Efficient Graph Neural Network Training at Large Scale |
2022 |
VLDB |
8.8955105e-05 |
| 5,443 |
Decoupled Graph Neural Networks for Large Dynamic Graphs |
2023 |
VLDB |
5.5025808e-05 |
| 5,018 |
DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks |
2023 |
SIGMOD |
5.7567672e-05 |
| 9,596 |
Scalable Graph Convolutional Network Training on Distributed-Memory Systems |
2023 |
VLDB |
4.319218e-05 |
| 5,710 |
DynaHB: A Communication-Avoiding Asynchronous Distributed Framework with Hybrid Batches for Dynamic GNN Training |
2024 |
VLDB |
5.3590055e-05 |
| 3,986 |
G3: When Graph Neural Networks Meet Parallel Graph Processing Systems on GPUs |
2020 |
VLDB |
6.5611714e-05 |
| 3,087 |
Scalable and Efficient Full-Graph GNN Training for Large Graphs |
2023 |
SIGMOD |
7.5939896e-05 |