arXiv:2207.03522
공식 코드 · Papers with Code
TF-GNN: Graph Neural Networks in TensorFlow
원문 초록
TensorFlow-GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow. It is designed from the bottom up to support the kinds of rich heterogeneous graph data that occurs in today's information ecosystems. In addition to enabling machine learning researchers and advanced developers, TF-GNN offers low-code solutions to empower the broader developer community in graph learning. Many production models at Google use TF-GNN, and it has been recently released as an open source project. In this paper we describe the TF-GNN data model, its Keras message passing API, and relevant capabilities such as graph sampling and distributed training.
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Graph Learning at Scale: Characterizing and Optimizing Pre-Propagation GNNs
Distributed Graph Neural Network Inference With Just-In-Time Compilation For Industry-Scale Graphs
Towards joint graph learning and sampling set selection from data
Graph World Model
S2FGL: Spatial Spectral Federated Graph Learning
Self-Supervised Graph Learning via Spectral Bootstrapping and Laplacian-Based Augmentations
토론
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