Introduction to Graph Neural Networks

Introduction to Graph Neural Networks
Author :
Publisher : Springer Nature
Total Pages : 109
Release :
ISBN-10 : 9783031015878
ISBN-13 : 3031015878
Rating : 4/5 (878 Downloads)

Book Synopsis Introduction to Graph Neural Networks by : Zhiyuan Zhiyuan Liu

Download or read book Introduction to Graph Neural Networks written by Zhiyuan Zhiyuan Liu and published by Springer Nature. This book was released on 2022-05-31 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. This book provides a comprehensive introduction to the basic concepts, models, and applications of graph neural networks. It starts with the introduction of the vanilla GNN model. Then several variants of the vanilla model are introduced such as graph convolutional networks, graph recurrent networks, graph attention networks, graph residual networks, and several general frameworks. Variants for different graph types and advanced training methods are also included. As for the applications of GNNs, the book categorizes them into structural, non-structural, and other scenarios, and then it introduces several typical models on solving these tasks. Finally, the closing chapters provide GNN open resources and the outlook of several future directions.


Introduction to Graph Neural Networks Related Books

Introduction to Graph Neural Networks
Language: en
Pages: 109
Authors: Zhiyuan Zhiyuan Liu
Categories: Computers
Type: BOOK - Published: 2022-05-31 - Publisher: Springer Nature

GET EBOOK

Graphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints, controlling traffic netw
Graph Representation Learning
Language: en
Pages: 141
Authors: William L. William L. Hamilton
Categories: Computers
Type: BOOK - Published: 2022-06-01 - Publisher: Springer Nature

GET EBOOK

Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational induct
Graph Neural Networks: Foundations, Frontiers, and Applications
Language: en
Pages: 701
Authors: Lingfei Wu
Categories: Computers
Type: BOOK - Published: 2022-01-03 - Publisher: Springer Nature

GET EBOOK

Deep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data
Introduction to Graph Neural Networks
Language: en
Pages: 129
Authors: Zhiyuan Liu
Categories: Computers
Type: BOOK - Published: 2020-03-20 - Publisher: Morgan & Claypool Publishers

GET EBOOK

This book provides a comprehensive introduction to the basic concepts, models, and applications of graph neural networks. It starts with the introduction of the
Distributed Computing and Intelligent Technology
Language: en
Pages: 280
Authors: Raju Bapi
Categories: Computers
Type: BOOK - Published: 2022-01-18 - Publisher: Springer Nature

GET EBOOK

This book constitutes the proceedings of the 18th International Conference on Distributed Computing and Intelligent Technology, ICDCIT 2022, held in Bhubaneswar