arXiv (All)
open access
A Dependency-Aware Multi-GPU Training Framework for Temporal Graph Neural Networks
Temporal graph neural networks (TGNNs) achieve superior performance in many dynamic graph learning tasks. These models use node state vectors (i.e., memories) to capture and record graph changes (i.e., events), and then exploit these memories to assist prediction. However, this memory-centric design
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- A Dependency-Aware Multi-GPU Training Framework for Temporal Graph Neural Networks#1318999
- Advancing graph neural networks : addressing representation challenges in the vision domain#413324
- DepTGL: A Parallel Framework for Memory-based TGNN Training with Adaptive Temporal Data Dependency Management#620705
- DepTGL: A Parallel Framework for Memory-based TGNN Training with Adaptive Temporal Data Dependency Management#619026
- DepTGL: A Parallel Framework for Memory-based TGNN Training with Adaptive Temporal Data Dependency Management#459613
Record · ID 1326980
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive