Jul 19, 2021 · This paper develops the Wide and Deep GNN (WD-GNN), a novel architecture that can be updated with distributed online learning mechanisms.
This paper develops the Wide and Deep GNN (WD-GNN), a novel architecture that can be updated with distributed online learning mechanisms. The WD-GNN consists of ...
Jun 11, 2020 · This paper proposes the Wide and Deep GNN (WD-GNN), a novel architecture that can be easily updated with distributed online learning mechanisms.
Aug 2, 2022 · We further propose a distributed online learning algorithm that can be implemented in a decentralized setting. We also show the stability of the ...
This paper proposes the Wide and Deep GNN (WD-GNN), a novel architecture that can be easily updated with distributed online learning mechanisms and derives ...
Graph neural networks (GNNs) are naturally distributed architectures for learning representations from network data. This renders them suitable candidates ...
Oct 13, 2024 · Graph neural networks (GNNs) are one of the rapidly growing fields within deep learning. While many distributed GNN training frameworks have ...
In this paper, we present P 3 , a system that focuses on scaling GNN model training to large real-world graphs in a distributed setting.
Missing: Wide Online
Jun 11, 2020 · Graph neural networks (GNNs) learn representations from network data with naturally distributed architectures, rendering them well-suited ...
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In this work, we propose a streamlined framework for distributed GNN training that eliminates these costly operations, yielding improved scalability, ...