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NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams
Summary: NeutronStream models evolving graphs as chronologically ordered event streams and applies an optimized sliding window with incremental dynamic graph storage to capture spatial–temporal dependencies for dynamic GNN training. Includes a parallel execution engine and user APIs, achieving 1.48–5.87× speedups and ≈4% average accuracy improvement.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 13701
- Venue
- VLDB
- Year
- 2024
- Pagerank
- 5.5514335e-05
- Overall Rank
- 5,356 | 62.78%
- DOI
-
10.14778/3632093.3632108
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 10 of 10 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 5,721 |
DynaHB: A Communication-Avoiding Asynchronous Distributed Framework with Hybrid Batches for Dynamic GNN Training |
2024 |
VLDB |
5.3538607e-05 |
| 5,746 |
Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective |
2024 |
VLDB |
5.3429324e-05 |
| 10,035 |
SWIFT: Enabling Large-Scale Temporal Graph Learning on a Single Machine |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,135 |
ABFlow: Alert Bursting Flow Query in Streaming Temporal Flow Networks |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,334 |
Understanding Evolving Graph Structures for Large Discrete-Time Dynamic Graph Representation |
2026 |
VLDB |
4.1905499e-05 |
| 10,515 |
SWASH: A Flexible Communication Framework with Sliding Window-Based Cache Sharing for Scalable DGNN Training |
2025 |
SIGMOD |
4.1905499e-05 |
| 10,642 |
PipeTGL: (Near) Zero Bubble Memory-based Temporal Graph Neural Network Training via Pipeline Optimization |
2025 |
VLDB |
4.1905499e-05 |
| 10,664 |
Effective and Efficient Distributed Temporal Graph Learning through Hotspot Memory Sharing |
2025 |
VLDB |
4.1905499e-05 |
| 10,681 |
When Speed meets Accuracy: an Efficient and Effective Graph Model for Temporal Link Prediction |
2025 |
VLDB |
4.1905499e-05 |
| 10,742 |
Faster Convergence in Mini-batch Graph Neural Networks Training with Pseudo Full Neighborhood Compensation |
2025 |
VLDB |
4.1905499e-05 |
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 |
| 271 |
AliGraph: A Comprehensive Graph Neural Network Platform |
2019 |
VLDB |
0.00029565193 |
| 635 |
APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding |
2021 |
SIGMOD |
0.00018825777 |
| 1,162 |
Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks |
2022 |
VLDB |
0.00013573136 |
| 1,388 |
TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs |
2022 |
VLDB |
0.00012249747 |
| 1,424 |
LiveGraph: A Transactional Graph Storage System with Purely Sequential Adjacency List Scans |
2020 |
VLDB |
0.00012044472 |
| 2,399 |
ByteGNN: Efficient Graph Neural Network Training at Large Scale |
2022 |
VLDB |
8.8869693e-05 |
| 2,425 |
DUCATI: A Dual-Cache Training System for Graph Neural Networks on Giant Graphs with the GPU |
2023 |
SIGMOD |
8.8414587e-05 |
| 2,678 |
HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework |
2022 |
VLDB |
8.3224016e-05 |
| 2,910 |
Teseo and the Analysis of Structural Dynamic Graphs |
2021 |
VLDB |
7.9276339e-05 |
| 3,028 |
NeutronStar: Distributed GNN Training with Hybrid Dependency Management |
2022 |
SIGMOD |
7.6833093e-05 |
| 3,281 |
Ginex: SSD-enabled Billion-scale Graph Neural Network Training on a Single Machine via Provably Optimal In-memory Caching |
2022 |
VLDB |
7.2812392e-05 |
| 3,715 |
Zebra: When Temporal Graph Neural Networks Meet Temporal Personalized PageRank |
2023 |
VLDB |
6.8176818e-05 |
| 7,359 |
An I/O-Efficient Disk-based Graph System for Scalable Second-Order Random Walk of Large Graphs |
2022 |
VLDB |
4.7477556e-05 |
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| Overall Rank |
Paper |
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Venue |
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| 3,092 |
Scalable and Efficient Full-Graph GNN Training for Large Graphs |
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SIGMOD |
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NeutronHeter: Optimizing Distributed Graph Neural Network Training for Heterogeneous Clusters |
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| 8,459 |
D3-GNN: Dynamic Distributed Dataflow for Streaming Graph Neural Networks |
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4.5008938e-05 |
| 5,135 |
NeutronOrch: Rethinking Sample-based GNN Training under CPU-GPU Heterogeneous Environments |
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| 3,028 |
NeutronStar: Distributed GNN Training with Hybrid Dependency Management |
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SIGMOD |
7.6833093e-05 |
| 10,310 |
NeutronCloud: Resource-Aware Distributed GNN Training in Fluctuating Cloud Environments |
2026 |
VLDB |
4.1905499e-05 |