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TF-GNN: Graph Neural Networks in TensorFlow

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arxiv 2207.03522 v2 pith:IS5P7QCI submitted 2022-07-07 cs.LG cs.NEcs.SIphysics.soc-phstat.ML

Oleksandr Ferludin , Arno Eigenwillig , Martin Blais , Dustin Zelle , Jan Pfeifer , Alvaro Sanchez-Gonzalez , Wai Lok Sibon Li , Sami Abu-El-Haija
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Peter Battaglia Neslihan Bulut Jonathan Halcrow Filipe Miguel Gonçalves de Almeida Pedro Gonnet Liangze Jiang Parth Kothari Silvio Lattanzi André Linhares Brandon Mayer Vahab Mirrokni John Palowitch Mihir Paradkar Jennifer She Anton Tsitsulin Kevin Villela Lisa Wang David Wong Bryan Perozzi
This is my paper · ORCID
classification cs.LGcs.NEcs.SIphysics.soc-phstat.ML
keywords graphtf-gnndatalearningnetworksneuraltensorflowaddition
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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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  1. Graph Neural Networks on Graph Databases

    cs.LG 2024-11 conditional novelty 6.0 of 10

    Training a GNN by querying a graph database for neighbor samples and features reduces memory use enough to train on small machines, but is much slower than in-memory training.

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