Responsible Graph Neural Networks

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Responsible Graph Neural Networks

Mohamed Abdel-Basset,Nour Moustafa,Hossam Hawash,Zahir Tari
Responsible Graph Neural Networks Book PDF

Author : Mohamed Abdel-Basset,Nour Moustafa,Hossam Hawash,Zahir Tari
Publisher : CRC Press
Page : 324 pages
File Size : 44,6 Mb
Release : 2023-06-05
Category : Computers
ISBN : 9781000871173

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Responsible Graph Neural Networks by Mohamed Abdel-Basset,Nour Moustafa,Hossam Hawash,Zahir Tari Pdf

More frequent and complex cyber threats require robust, automated, and rapid responses from cyber-security specialists. This book offers a complete study in the area of graph learning in cyber, emphasizing graph neural networks (GNNs) and their cyber-security applications. Three parts examine the basics, methods and practices, and advanced topics. The first part presents a grounding in graph data structures and graph embedding and gives a taxonomic view of GNNs and cyber-security applications. The second part explains three different categories of graph learning, including deterministic, generative, and reinforcement learning and how they can be used for developing cyber defense models. The discussion of each category covers the applicability of simple and complex graphs, scalability, representative algorithms, and technical details. Undergraduate students, graduate students, researchers, cyber analysts, and AI engineers looking to understand practical deep learning methods will find this book an invaluable resource.

Introduction to Graph Neural Networks

Zhiyuan Liu,Jie Zhou
Introduction to Graph Neural Networks Book PDF

Author : Zhiyuan Liu,Jie Zhou
Publisher : Morgan & Claypool Publishers
Page : 129 pages
File Size : 48,6 Mb
Release : 2020-03-20
Category : Computers
ISBN : 9781681737669

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Introduction to Graph Neural Networks by Zhiyuan Liu,Jie Zhou Pdf

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.

Graph Neural Networks: Foundations, Frontiers, and Applications

Lingfei Wu,Peng Cui,Jian Pei,Liang Zhao
Graph Neural Networks Foundations Frontiers and Applications Book PDF

Author : Lingfei Wu,Peng Cui,Jian Pei,Liang Zhao
Publisher : Springer Nature
Page : 701 pages
File Size : 45,8 Mb
Release : 2022-01-03
Category : Computers
ISBN : 9789811660542

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Graph Neural Networks: Foundations, Frontiers, and Applications by Lingfei Wu,Peng Cui,Jian Pei,Liang Zhao Pdf

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, such as images or sequence data, and not immediately applicable to graph-structured data such as text. This gap has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification. The new neural network architectures on graph-structured data (graph neural networks, GNNs in short) have performed remarkably on these tasks, demonstrated by applications in social networks, bioinformatics, and medical informatics. Despite these successes, GNNs still face many challenges ranging from the foundational methodologies to the theoretical understandings of the power of the graph representation learning. This book provides a comprehensive introduction of GNNs. It first discusses the goals of graph representation learning and then reviews the history, current developments, and future directions of GNNs. The second part presents and reviews fundamental methods and theories concerning GNNs while the third part describes various frontiers that are built on the GNNs. The book concludes with an overview of recent developments in a number of applications using GNNs. This book is suitable for a wide audience including undergraduate and graduate students, postdoctoral researchers, professors and lecturers, as well as industrial and government practitioners who are new to this area or who already have some basic background but want to learn more about advanced and promising techniques and applications.

Bridging the Gap between Machine Learning and Affective Computing

Zhen Cui,Abhinav Dhall,Xiaopeng Hong,Yong Li,Wenming Zheng,Yuan Zong
Bridging the Gap between Machine Learning and Affective Computing Book PDF

Author : Zhen Cui,Abhinav Dhall,Xiaopeng Hong,Yong Li,Wenming Zheng,Yuan Zong
Publisher : Frontiers Media SA
Page : 151 pages
File Size : 49,5 Mb
Release : 2023-01-05
Category : Science
ISBN : 9782832503799

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Bridging the Gap between Machine Learning and Affective Computing by Zhen Cui,Abhinav Dhall,Xiaopeng Hong,Yong Li,Wenming Zheng,Yuan Zong Pdf

Affective computing refers to computing that relates to, arises from, or influences emotions, as pioneered by Rosalind Picard in 1995. The goal of affective computing is to bridge the gap between human and machines and ultimately enable robots to communicate with human naturally and emotionally. Recently, the research on affective computing has gained considerable progress with many fields contributing including neuroscience, psychology, education, medicine, behavior, sociology, and computer science. Current research in affective computing mainly focuses on estimating of human emotions through different forms of signals, e.g., face video, EEG, Speech, PET scans or fMRI. Inferring the emotion of humans is difficult, as emotion is a subjective, unconscious experience characterized primarily by psycho-physiological expressions and biological reactions. It is influenced by hormones and neurotransmitters such as dopamine, noradrenaline, serotonin, oxytocin, GABA… etc. The physiology of emotion is closely linked to arousal of the nervous system with various states and strengths relating, apparently, to different particular emotions. To understand “emotion” or “affect” merely by machine learning or big data analysis is not enough, but the understanding and applications from the intrinsic features of emotions from the neuroscience aspect is essential.

Graph Neural Networks in Action

Keita Broadwater,Namid Stillman
Graph Neural Networks in Action Book PDF

Author : Keita Broadwater,Namid Stillman
Publisher : Manning
Page : 0 pages
File Size : 43,8 Mb
Release : 2023-03-28
Category : Computers
ISBN : 1617299057

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Graph Neural Networks in Action by Keita Broadwater,Namid Stillman Pdf

A hands-on guide to powerful graph-based deep learning models! Learn how to build cutting-edge graph neural networks for recommendation engines, molecular modeling, and more. Graph Neural Networks in Action teaches you to create powerful deep learning models for working with graph data. You’ll learn how to both design and train your models, and how to develop them into practical applications you can deploy to production. In Graph Neural Networks in Action you’ll create deep learning models that are perfect for working with interconnected graph data. Start with a comprehensive introduction to graph data’s unique properties. Then, dive straight into building real-world models, including GNNs that can generate node embeddings from a social network, recommend eCommerce products, and draw insights from social sites. This comprehensive guide contains coverage of the essential GNN libraries, including PyTorch Geometric, DeepGraph Library, and Alibaba’s GraphScope for training at scale. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Artificial Neural Networks and Machine Learning – ICANN 2022

Elias Pimenidis,Plamen Angelov,Chrisina Jayne,Antonios Papaleonidas,Mehmet Aydin
Artificial Neural Networks and Machine Learning ICANN 2022 Book PDF

Author : Elias Pimenidis,Plamen Angelov,Chrisina Jayne,Antonios Papaleonidas,Mehmet Aydin
Publisher : Springer Nature
Page : 836 pages
File Size : 46,8 Mb
Release : 2022-09-06
Category : Computers
ISBN : 9783031159312

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Artificial Neural Networks and Machine Learning – ICANN 2022 by Elias Pimenidis,Plamen Angelov,Chrisina Jayne,Antonios Papaleonidas,Mehmet Aydin Pdf

The 4-volumes set of LNCS 13529, 13530, 13531, and 13532 constitutes the proceedings of the 31st International Conference on Artificial Neural Networks, ICANN 2022, held in Bristol, UK, in September 2022. The total of 255 full papers presented in these proceedings was carefully reviewed and selected from 561 submissions. ICANN 2022 is a dual-track conference featuring tracks in brain inspired computing and machine learning and artificial neural networks, with strong cross-disciplinary interactions and applications. Chapters “Learning Flexible Translation Between Robot Actions and Language Descriptions”, “Learning Visually Grounded Human-Robot Dialog in a Hybrid Neural Architecture” are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

MEDINFO 89

Barry Barber
MEDINFO 89 Book PDF

Author : Barry Barber
Publisher : North Holland
Page : 840 pages
File Size : 53,8 Mb
Release : 1989
Category : Computers
ISBN : NWU:35558002148076

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MEDINFO 89 by Barry Barber Pdf

The IFIP World Conference Series on Medical Informatics is a series of volumes, which has been published triennially since 1974. This sixth volume in the series is a comprehensive overview of Medical Informatics containing refereed research papers, the best of over 800 submissions from 47 countries.

Deep Learning on Graphs

Yao Ma,Jiliang Tang
Deep Learning on Graphs Book PDF

Author : Yao Ma,Jiliang Tang
Publisher : Cambridge University Press
Page : 340 pages
File Size : 46,5 Mb
Release : 2021-09-23
Category : Computers
ISBN : 9781108934824

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Deep Learning on Graphs by Yao Ma,Jiliang Tang Pdf

Deep learning on graphs has become one of the hottest topics in machine learning. The book consists of four parts to best accommodate our readers with diverse backgrounds and purposes of reading. Part 1 introduces basic concepts of graphs and deep learning; Part 2 discusses the most established methods from the basic to advanced settings; Part 3 presents the most typical applications including natural language processing, computer vision, data mining, biochemistry and healthcare; and Part 4 describes advances of methods and applications that tend to be important and promising for future research. The book is self-contained, making it accessible to a broader range of readers including (1) senior undergraduate and graduate students; (2) practitioners and project managers who want to adopt graph neural networks into their products and platforms; and (3) researchers without a computer science background who want to use graph neural networks to advance their disciplines.

Journal of Artificial Neural Networks

Anonim
Journal of Artificial Neural Networks Book PDF

Author : Anonim
Publisher : Unknown
Page : 616 pages
File Size : 51,8 Mb
Release : 1994
Category : Neural networks (Computer science)
ISBN : PSU:000018889895

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Journal of Artificial Neural Networks by Anonim Pdf

Representation of Scientific Texts in Knowledge Graphs

Pieter Hendrik de Vries
Representation of Scientific Texts in Knowledge Graphs Book PDF

Author : Pieter Hendrik de Vries
Publisher : Unknown
Page : 172 pages
File Size : 46,5 Mb
Release : 1989
Category : Conceptual structures (Information theory)
ISBN : STANFORD:36105043305643

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Representation of Scientific Texts in Knowledge Graphs by Pieter Hendrik de Vries Pdf

Neural Networks for Conditional Probability Estimation

Dirk Husmeier
Neural Networks for Conditional Probability Estimation Book PDF

Author : Dirk Husmeier
Publisher : Springer
Page : 308 pages
File Size : 47,5 Mb
Release : 1999-02-22
Category : Computers
ISBN : STANFORD:36105122674844

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Neural Networks for Conditional Probability Estimation by Dirk Husmeier Pdf

This volume presents a neural network architecture for the prediction of conditional probability densities - which is vital when carrying out universal approximation on variables which are either strongly skewed or multimodal. Two alternative approaches are discussed: the GM network, in which all parameters are adapted in the training scheme, and the GM-RVFL model which draws on the random functional link net approach. Points of particular interest are: - it examines the modification to standard approaches needed for conditional probability prediction; - it provides the first real-world test results for recent theoretical findings about the relationship between generalisation performance of committees and the over-flexibility of their members; This volume will be of interest to all researchers, practitioners and postgraduate / advanced undergraduate students working on applications of neural networks - especially those related to finance and pattern recognition.

International Joint Conference on Neural Networks

International Joint Conference on Neural Networks 1990, Washington, DC
International Joint Conference on Neural Networks Book PDF

Author : International Joint Conference on Neural Networks 1990, Washington, DC
Publisher : Unknown
Page : 812 pages
File Size : 48,7 Mb
Release : 1990
Category : Electronic
ISBN : 0805807543

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International Joint Conference on Neural Networks by International Joint Conference on Neural Networks 1990, Washington, DC Pdf