Companies Home Search Profile

Introduction to Graph Neural Networks (Synthesis Lectures on Artificial Intelligence and Machine Learning)

Introduction to Graph Neural Networks (Synthesis Lectures on Artificial Intelligence and Machine Learning)

Download pdf

Introduction to Graph Neural Networks (Synthesis Lectures on Artificial Intelligence and Machine Learning)

Introduction to Graph Neural Networks (Synthesis Lectures on Artificial Intelligence and Machine Learning)

Category

Artificial Intelligence

Neural Networks

Author

Zhiyuan Liu

Publication

Morgan & Claypool Publishers

0 View

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.

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.

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.

'

Download pdf

ISBN-10

1681737671

ISBN-13

978-1681737676

Publisher

Morgan & Claypool Publishers

Price

64.95

File Type

PDF

Page No.

128

Zhiyuan Liu

Similar Books

Artificial Intelligence and Machine Learning Methods in COVID-19 and Related Health Diseases (Studies in Computational Intelligence Book 1023)

Artificial Intelligence and Machine Learning Methods in COVID-19 and Related Health Diseases (Studies in Computational Intelligence Book 1023)

Victor Chang

Machine Learning

Applied Deep Learning: Tools, Techniques, and Implementation (Computational Intelligence Methods and Applications)

Applied Deep Learning: Tools, Techniques, and Implementation (Computational Intelligence Methods and Applications)

Paul Fergus

Artificial Intelligence

Intelligence - Theories and Applications

Intelligence - Theories and Applications

Rainer M. Holm-Hadulla

Artificial Intelligence

Regulation of Innovative Technologies: Blockchain, Artificial Intelligence and Quantum Computing

Regulation of Innovative Technologies: Blockchain, Artificial Intelligence and Quantum Computing

Rosario Girasa

Machine Learning and Non-volatile Memories

Machine Learning and Non-volatile Memories

Rino Micheloni

Machine Learning

Artificial Intelligence in Music, Sound, Art and Design: 11th International Conference, EvoMUSART 2022, Held as Part of EvoStar 2022, Madrid, Spain, April ... Notes in Computer Science Book 13221)

Artificial Intelligence in Music, Sound, Art and Design: 11th International Conference, EvoMUSART 2022, Held as Part of EvoStar 2022, Madrid, Spain, April ... Notes in Computer Science Book 13221)

Tiago Martins

Advances in Intelligent Data Analysis XX: 20th International Symposium on Intelligent Data Analysis, IDA 2022, Rennes, France, April 20–22, 2022, Proceedings (Lecture Notes in Computer Science, 13205)

Advances in Intelligent Data Analysis XX: 20th International Symposium on Intelligent Data Analysis, IDA 2022, Rennes, France, April 20–22, 2022, Proceedings (Lecture Notes in Computer Science, 13205)

Tassadit Bouadi

Artificial Intelligence

Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging

Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging

Patrick Veit-Haibach

Machine Learning

Security and Artificial Intelligence: A Crossdisciplinary Approach (Lecture Notes in Computer Science, 13049)

Security and Artificial Intelligence: A Crossdisciplinary Approach (Lecture Notes in Computer Science, 13049)

Lejla Batina

Artificial Intelligence

RoboCup 2021: Robot World Cup XXIV (Lecture Notes in Computer Science)

RoboCup 2021: Robot World Cup XXIV (Lecture Notes in Computer Science)

Oliver Obst

Artificial Intelligence

Language, Logic, and Computation: 13th International Tbilisi Symposium, TbiLLC 2019, Batumi, Georgia, September 16–20, 2019, Revised Selected Papers (Lecture Notes in Computer Science Book 13206)

Language, Logic, and Computation: 13th International Tbilisi Symposium, TbiLLC 2019, Batumi, Georgia, September 16–20, 2019, Revised Selected Papers (Lecture Notes in Computer Science Book 13206)

Aybüke Özgün

Artificial Intelligence

Advanced Artificial Intelligence and Robo-Justice

Advanced Artificial Intelligence and Robo-Justice

Georgios I. Zekos

Artificial Intelligence

Advances in Neural Computation, Machine Learning, and Cognitive Research V: Selected Papers from the XXIII International Conference on Neuroinformatics, ... in Computational Intelligence Book 1008)

Advances in Neural Computation, Machine Learning, and Cognitive Research V: Selected Papers from the XXIII International Conference on Neuroinformatics, ... in Computational Intelligence Book 1008)

Boris Kryzhanovsky

Machine Learning

Artificial Neural Networks and Machine Learning – ICANN 2021: 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, ... (Lecture Notes in Computer Science, 12893)

Artificial Neural Networks and Machine Learning – ICANN 2021: 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, ... (Lecture Notes in Computer Science, 12893)

Igor Farkaš

Machine Learning

Artificial Vision and Language Processing for Robotics: Create end-to-end systems that can power robots with artificial vision and deep learning techniques

Artificial Vision and Language Processing for Robotics: Create end-to-end systems that can power robots with artificial vision and deep learning techniques

Unai Garay Maestre

Transformers for Natural Language Processing: Build, train, and fine-tune deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, and GPT-3, 2nd Edition

Transformers for Natural Language Processing: Build, train, and fine-tune deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, and GPT-3, 2nd Edition

Denis Rothman

Hands-On Python Deep Learning for the Web: Integrating neural network architectures to build smart web apps with Flask, Django, and TensorFlow

Hands-On Python Deep Learning for the Web: Integrating neural network architectures to build smart web apps with Flask, Django, and TensorFlow

Sayak Paul

Hands-On Deep Learning for IoT: Train neural network models to develop intelligent IoT applications

Hands-On Deep Learning for IoT: Train neural network models to develop intelligent IoT applications

Md. Rezaul Karim

Internet Of Things

Hands-On Neural Networks with TensorFlow 2.0: Understand TensorFlow, from static graph to eager execution, and design neural networks

Hands-On Neural Networks with TensorFlow 2.0: Understand TensorFlow, from static graph to eager execution, and design neural networks

Paolo Galeone

Mastering Machine Learning on AWS: Advanced machine learning in Python using SageMaker, Apache Spark, and TensorFlow

Mastering Machine Learning on AWS: Advanced machine learning in Python using SageMaker, Apache Spark, and TensorFlow

Dr. Saket S.R. Mengle

Hands-On Deep Learning for Games: Leverage the power of neural networks and reinforcement learning to build intelligent games

Hands-On Deep Learning for Games: Leverage the power of neural networks and reinforcement learning to build intelligent games

Micheal Lanham

Deep Learning Quick Reference: Useful hacks for training and optimizing deep neural networks with TensorFlow and Keras

Deep Learning Quick Reference: Useful hacks for training and optimizing deep neural networks with TensorFlow and Keras

Mike Bernico

Deep Learning For Eeg-based Brain-computer Interfaces: Representations, Algorithms And Applications

Deep Learning For Eeg-based Brain-computer Interfaces: Representations, Algorithms And Applications

Xiang Zhang

Other Authors' Books

Other Publishing Books

Quantum Computing for Computer Architects

Quantum Computing for Computer Architects

Tzvetan S. Metodi

Computer Architecture

Transactional Memory (Synthesis Lectures on Computer Architecture, 2)

Transactional Memory (Synthesis Lectures on Computer Architecture, 2)

James Larus

Computer Architecture

Link Reversal Algorithms (Synthesis Lectures on Distributed Computing Theory, 8)

Link Reversal Algorithms (Synthesis Lectures on Distributed Computing Theory, 8)

Jennifer E. Walter

Embedded Systems Design with the Atmel AVR Microcontroller (Synthesis Lectures on Digital Circuits and Systems, 24)

Embedded Systems Design with the Atmel AVR Microcontroller (Synthesis Lectures on Digital Circuits and Systems, 24)

Steven Barrett

PIC Microcontroller

Advanced Metasearch Engine Technology (Synthessis Lectures on Data Management)

Advanced Metasearch Engine Technology (Synthessis Lectures on Data Management)

Weiyi Meng

Software Engineering

A Guide to Visual Multi-Level Interface Design From Synthesis of Empirical Study (Synthesis Lectures on Visualization)

A Guide to Visual Multi-Level Interface Design From Synthesis of Empirical Study (Synthesis Lectures on Visualization)

Tamara Munzner

Data Visualization

Analysis of the MPEG-1 Layer III (MP3) Algorithm Using MATLAB (Synthesis Lectures on Algorithms and Software in Engineering)

Analysis of the MPEG-1 Layer III (MP3) Algorithm Using MATLAB (Synthesis Lectures on Algorithms and Software in Engineering)

Jayaraman J. Thiagarajan

High-level Structures in Quantum Computing

High-level Structures in Quantum Computing

Jaroslaw Adam Miszczak

Visual Information Retrieval using Java and LIRE (Synthesis Lectures on Information Concepts, Retrieval, and Services, 25)

Visual Information Retrieval using Java and LIRE (Synthesis Lectures on Information Concepts, Retrieval, and Services, 25)

Mathias Lux

Signal Processing for Solar Array Monitoring, Fault Detection, and Optimization (Synthesis Lectures on Power Electronics, 4)

Signal Processing for Solar Array Monitoring, Fault Detection, and Optimization (Synthesis Lectures on Power Electronics, 4)

Andreas Spanias

Electrical Engineering

A Primer on Hardware Prefetching (Synthesis Lectures on Computer Architecture)

A Primer on Hardware Prefetching (Synthesis Lectures on Computer Architecture)

Babak Falsafi

Computer Architecture

Distributed Graph Coloring: Fundamentals and Recent Developments (Synthesis Lectures on Digital Computing Theory)

Distributed Graph Coloring: Fundamentals and Recent Developments (Synthesis Lectures on Digital Computing Theory)

Leonid Barenboim

CFA Chartered Financial Analyst

User Reviews

Log in first to post a comment

Rating

0

0

0

0

0

average 0

Total votes0