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Found 6 papers, 2 papers with code
LiGNN: Graph Neural Networks at LinkedIn
In this paper, we present LiGNN, a deployed large-scale Graph Neural Networks (GNNs) Framework.
Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks
To solve the performance degradation, we propose to apply $\text{{Global Server Corrections}}$ on the server to refine the locally learned models.
On Provable Benefits of Depth in Training Graph Convolutional Networks
Graph Convolutional Networks (GCNs) are known to suffer from performance degradation as the number of layers increases, which is usually attributed to over-smoothing.
On the Importance of Sampling in Training GCNs: Tighter Analysis and Variance Reduction
In this paper, we describe and analyze a general doubly variance reduction schema that can accelerate any sampling method under the memory budget.
On the Importance of Sampling in Training GCNs: Convergence Analysis and Variance Reduction
In this paper, we describe and analyze a general \textbf{\textit{doubly variance reduction}} schema that can accelerate any sampling method under the memory budget.
GCN meets GPU: Decoupling “When to Sample” from “How to Sample”
Sampling-based methods promise scalability improvements when paired with stochastic gradient descent in training Graph Convolutional Networks (GCNs).
