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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Introduction to Graph Neural Networks

Introduction to Graph Neural Networks

Liu, Zhiyuan
Zhou, Jie

Paperback

Series: Synthesis Lectures on Artificial Intelligence and Machine Le

General ComputersGeneral MathematicsProbability & Statistics

ISBN10: 3031004590
ISBN13: 9783031004599
Publisher: Springer
Published: Mar 20 2020
Pages: 109
Weight: 0.51
Height: 0.27 Width: 7.50 Depth: 9.25
Language: English

Graphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints, controlling traffic networks, and recommending friends in social networks. However, these tasks require dealing with non-Euclidean graph data that contains rich relational information between elements and cannot be well handled by traditional deep learning models (e.g., convolutional neural networks (CNNs) or recurrent neural networks (RNNs)). Nodes in graphs usually contain useful feature information that cannot be well addressed in most unsupervised representation learning methods (e.g., network embedding methods). Graph neural networks (GNNs) are proposed to combine the feature information and the graph structure to learn better representations on graphs via feature propagation and aggregation. Due to its convincing performance and high interpretability, GNN has recently become a widely applied graph analysis tool.

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