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Orca: Scalable Temporal Graph Neural Network Training with Theoretical Guarantees
Summary: Orca accelerates temporal GNN training on dynamic graphs by caching and reusing intermediate embeddings under practical limits. MRD cache replacement with theoretical error bounds and convergence guarantees; yields large speedup and improved accuracy.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 6556
- Venue
- SIGMOD
- Year
- 2023
- Pagerank
- 6.4910804e-05
- Overall Rank
- 4,054 | 71.83%
- DOI
-
10.1145/3588737
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 14 of 14 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 5,485 |
ETC: Efficient Training of Temporal Graph Neural Networks over Large-scale Dynamic Graphs |
2024 |
VLDB |
5.4817019e-05 |
| 5,721 |
DynaHB: A Communication-Avoiding Asynchronous Distributed Framework with Hybrid Batches for Dynamic GNN Training |
2024 |
VLDB |
5.3538607e-05 |
| 6,944 |
Efficient Training of Graph Neural Networks on Large Graphs |
2024 |
VLDB |
4.8875946e-05 |
| 7,014 |
SIMPLE: Efficient Temporal Graph Neural Network Training at Scale with Dynamic Data Placement |
2024 |
SIGMOD |
4.8570865e-05 |
| 7,568 |
ADGNN: Towards Scalable GNN Training with Aggregation-Difference Aware Sampling |
2023 |
SIGMOD |
4.7044808e-05 |
| 8,507 |
Fight Fire with Fire: Towards Robust Graph Neural Networks on Dynamic Graphs via Actively Defense |
2024 |
VLDB |
4.4909322e-05 |
| 9,677 |
Apt-Serve: Adaptive Request Scheduling on Hybrid Cache for Scalable LLM Inference Serving |
2025 |
SIGMOD |
4.3006524e-05 |
| 9,786 |
MEMO: Fine-grained Tensor Management For Ultra-long Context LLM Training |
2025 |
SIGMOD |
4.2799988e-05 |
| 10,035 |
SWIFT: Enabling Large-Scale Temporal Graph Learning on a Single Machine |
2026 |
SIGMOD |
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 |
| 10,891 |
Towards Ideal Temporal Graph Neural Networks: Evaluations and Conclusions after 10,000 GPU Hours |
2025 |
VLDB |
4.1905499e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 3,092 |
Scalable and Efficient Full-Graph GNN Training for Large Graphs |
2023 |
SIGMOD |
7.5869574e-05 |
| 10,035 |
SWIFT: Enabling Large-Scale Temporal Graph Learning on a Single Machine |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,334 |
Understanding Evolving Graph Structures for Large Discrete-Time Dynamic Graph Representation |
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When Speed meets Accuracy: an Efficient and Effective Graph Model for Temporal Link Prediction |
2025 |
VLDB |
4.1905499e-05 |
| 5,485 |
ETC: Efficient Training of Temporal Graph Neural Networks over Large-scale Dynamic Graphs |
2024 |
VLDB |
5.4817019e-05 |
| 5,452 |
Decoupled Graph Neural Networks for Large Dynamic Graphs |
2023 |
VLDB |
5.4972958e-05 |
| 1,388 |
TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs |
2022 |
VLDB |
0.00012249747 |
| 5,328 |
FreshGNN: Reducing Memory Access via Stable Historical Embeddings for Graph Neural Network Training |
2024 |
VLDB |
5.5656888e-05 |
| 10,891 |
Towards Ideal Temporal Graph Neural Networks: Evaluations and Conclusions after 10,000 GPU Hours |
2025 |
VLDB |
4.1905499e-05 |
| 7,014 |
SIMPLE: Efficient Temporal Graph Neural Network Training at Scale with Dynamic Data Placement |
2024 |
SIGMOD |
4.8570865e-05 |