Graph Neural Networks In Action

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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 : 46,6 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.

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 : 48,8 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 : 51,8 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.

Graph Representation Learning

William L. William L. Hamilton
Graph Representation Learning Book PDF

Author : William L. William L. Hamilton
Publisher : Springer Nature
Page : 141 pages
File Size : 40,7 Mb
Release : 2022-06-01
Category : Computers
ISBN : 9783031015885

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Graph Representation Learning by William L. William L. Hamilton Pdf

Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

Deep Learning on Graphs

Yao Ma,Jiliang Tang
Deep Learning on Graphs Book PDF

Author : Yao Ma,Jiliang Tang
Publisher : Cambridge University Press
Page : 339 pages
File Size : 54,9 Mb
Release : 2021-09-23
Category : Computers
ISBN : 9781108831741

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Deep Learning on Graphs by Yao Ma,Jiliang Tang Pdf

A comprehensive text on foundations and techniques of graph neural networks with applications in NLP, data mining, vision and healthcare.

Concepts and Techniques of Graph Neural Networks

Kumar, Vinod,Rajput, Dharmendra Singh
Concepts and Techniques of Graph Neural Networks Book PDF

Author : Kumar, Vinod,Rajput, Dharmendra Singh
Publisher : IGI Global
Page : 267 pages
File Size : 47,8 Mb
Release : 2023-05-22
Category : Computers
ISBN : 9781668469057

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Concepts and Techniques of Graph Neural Networks by Kumar, Vinod,Rajput, Dharmendra Singh Pdf

Recent advancements in graph neural networks have expanded their capacities and expressive power. Furthermore, practical applications have begun to emerge in a variety of fields including recommendation systems, fake news detection, traffic prediction, molecular structure in chemistry, antibacterial discovery physics simulations, and more. As a result, a boom of research at the juncture of graph theory and deep learning has revolutionized many areas of research. However, while graph neural networks have drawn a lot of attention, they still face many challenges when it comes to applying them to other domains, from a conceptual understanding of methodologies to scalability and interpretability in a real system. Concepts and Techniques of Graph Neural Networks provides a stepwise discussion, an exhaustive literature review, detailed analysis and discussion, rigorous experimentation results, and application-oriented approaches that are demonstrated with respect to applications of graph neural networks. The book also develops the understanding of concepts and techniques of graph neural networks and establishes the familiarity of different real applications in various domains for graph neural networks. Covering key topics such as graph data, social networks, deep learning, and graph clustering, this premier reference source is ideal for industry professionals, researchers, scholars, academicians, practitioners, instructors, and students.

MultiMedia Modeling

Yong Man Ro,Wen-Huang Cheng,Junmo Kim,Wei-Ta Chu,Peng Cui,Jung-Woo Choi,Min-Chun Hu,Wesley De Neve
MultiMedia Modeling Book PDF

Author : Yong Man Ro,Wen-Huang Cheng,Junmo Kim,Wei-Ta Chu,Peng Cui,Jung-Woo Choi,Min-Chun Hu,Wesley De Neve
Publisher : Springer Nature
Page : 860 pages
File Size : 44,5 Mb
Release : 2019-12-27
Category : Computers
ISBN : 9783030377311

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MultiMedia Modeling by Yong Man Ro,Wen-Huang Cheng,Junmo Kim,Wei-Ta Chu,Peng Cui,Jung-Woo Choi,Min-Chun Hu,Wesley De Neve Pdf

The two-volume set LNCS 11961 and 11962 constitutes the thoroughly refereed proceedings of the 25th International Conference on MultiMedia Modeling, MMM 2020, held in Daejeon, South Korea, in January 2020. Of the 171 submitted full research papers, 40 papers were selected for oral presentation and 46 for poster presentation; 28 special session papers were selected for oral presentation and 8 for poster presentation; in addition, 9 demonstration papers and 6 papers for the Video Browser Showdown 2020 were accepted. The papers of LNCS 11961 are organized in the following topical sections: audio and signal processing; coding and HVS; color processing and art; detection and classification; face; image processing; learning and knowledge representation; video processing; poster papers; the papers of LNCS 11962 are organized in the following topical sections: poster papers; AI-powered 3D vision; multimedia analytics: perspectives, tools and applications; multimedia datasets for repeatable experimentation; multi-modal affective computing of large-scale multimedia data; multimedia and multimodal analytics in the medical domain and pervasive environments; intelligent multimedia security; demo papers; and VBS papers.

Artificial Neural Networks and Machine Learning – ICANN 2021

Igor Farkaš,Paolo Masulli,Sebastian Otte,Stefan Wermter
Artificial Neural Networks and Machine Learning ICANN 2021 Book PDF

Author : Igor Farkaš,Paolo Masulli,Sebastian Otte,Stefan Wermter
Publisher : Springer Nature
Page : 708 pages
File Size : 50,7 Mb
Release : 2021-09-10
Category : Computers
ISBN : 9783030863654

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Artificial Neural Networks and Machine Learning – ICANN 2021 by Igor Farkaš,Paolo Masulli,Sebastian Otte,Stefan Wermter Pdf

The proceedings set LNCS 12891, LNCS 12892, LNCS 12893, LNCS 12894 and LNCS 12895 constitute the proceedings of the 30th International Conference on Artificial Neural Networks, ICANN 2021, held in Bratislava, Slovakia, in September 2021.* The total of 265 full papers presented in these proceedings was carefully reviewed and selected from 496 submissions, and organized in 5 volumes. In this volume, the papers focus on topics such as generative neural networks, graph neural networks, hierarchical and ensemble models, human pose estimation, image processing, image segmentation, knowledge distillation, and medical image processing. *The conference was held online 2021 due to the COVID-19 pandemic.

Artificial Intelligence

Lu Fang,Daniel Povey,Guangtao Zhai,Tao Mei,Ruiping Wang
Artificial Intelligence Book PDF

Author : Lu Fang,Daniel Povey,Guangtao Zhai,Tao Mei,Ruiping Wang
Publisher : Springer Nature
Page : 660 pages
File Size : 40,6 Mb
Release : 2023-01-01
Category : Computers
ISBN : 9783031205002

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Artificial Intelligence by Lu Fang,Daniel Povey,Guangtao Zhai,Tao Mei,Ruiping Wang Pdf

This three-volume set LNCS 13604-13606 constitutes revised selected papers presented at the Second CAAI International Conference on Artificial Intelligence, held in Beijing, China, in August 2022. CICAI is a summit forum in the field of artificial intelligence and the 2022 forum was hosted by Chinese Association for Artificial Intelligence (CAAI). The 164 papers were thoroughly reviewed and selected from 521 submissions. CICAI aims to establish a global platform for international academic exchange, promote advanced research in AI and its affiliated disciplines such as machine learning, computer vision, natural language, processing, and data mining, amongst others.

Deep Learning for Robot Perception and Cognition

Alexandros Iosifidis,Anastasios Tefas
Deep Learning for Robot Perception and Cognition Book PDF

Author : Alexandros Iosifidis,Anastasios Tefas
Publisher : Academic Press
Page : 638 pages
File Size : 53,9 Mb
Release : 2022-02-04
Category : Computers
ISBN : 9780323885720

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Deep Learning for Robot Perception and Cognition by Alexandros Iosifidis,Anastasios Tefas Pdf

Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks. Presents deep learning principles and methodologies Explains the principles of applying end-to-end learning in robotics applications Presents how to design and train deep learning models Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more Uses robotic simulation environments for training deep learning models Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis

Graph Machine Learning

Claudio Stamile,Aldo Marzullo,Enrico Deusebio
Graph Machine Learning Book PDF

Author : Claudio Stamile,Aldo Marzullo,Enrico Deusebio
Publisher : Packt Publishing Ltd
Page : 338 pages
File Size : 53,7 Mb
Release : 2021-06-25
Category : Computers
ISBN : 9781800206755

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Graph Machine Learning by Claudio Stamile,Aldo Marzullo,Enrico Deusebio Pdf

Build machine learning algorithms using graph data and efficiently exploit topological information within your models Key Features Implement machine learning techniques and algorithms in graph data Identify the relationship between nodes in order to make better business decisions Apply graph-based machine learning methods to solve real-life problems Book Description Graph Machine Learning will introduce you to a set of tools used for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks. The first chapters will introduce you to graph theory and graph machine learning, as well as the scope of their potential use. You'll then learn all you need to know about the main machine learning models for graph representation learning: their purpose, how they work, and how they can be implemented in a wide range of supervised and unsupervised learning applications. You'll build a complete machine learning pipeline, including data processing, model training, and prediction in order to exploit the full potential of graph data. After covering the basics, you'll be taken through real-world scenarios such as extracting data from social networks, text analytics, and natural language processing (NLP) using graphs and financial transaction systems on graphs. You'll also learn how to build and scale out data-driven applications for graph analytics to store, query, and process network information, and explore the latest trends on graphs. By the end of this machine learning book, you will have learned essential concepts of graph theory and all the algorithms and techniques used to build successful machine learning applications. What you will learn Write Python scripts to extract features from graphs Distinguish between the main graph representation learning techniques Learn how to extract data from social networks, financial transaction systems, for text analysis, and more Implement the main unsupervised and supervised graph embedding techniques Get to grips with shallow embedding methods, graph neural networks, graph regularization methods, and more Deploy and scale out your application seamlessly Who this book is for This book is for data scientists, data analysts, graph analysts, and graph professionals who want to leverage the information embedded in the connections and relations between data points to boost their analysis and model performance using machine learning. It will also be useful for machine learning developers or anyone who wants to build ML-driven graph databases. A beginner-level understanding of graph databases and graph data is required, alongside a solid understanding of ML basics. You'll also need intermediate-level Python programming knowledge to get started with this book.

Advances in Graph Neural Networks

Chuan Shi,Xiao Wang,Cheng Yang
Advances in Graph Neural Networks Book PDF

Author : Chuan Shi,Xiao Wang,Cheng Yang
Publisher : Springer Nature
Page : 207 pages
File Size : 50,8 Mb
Release : 2022-11-16
Category : Mathematics
ISBN : 9783031161742

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Advances in Graph Neural Networks by Chuan Shi,Xiao Wang,Cheng Yang Pdf

This book provides a comprehensive introduction to the foundations and frontiers of graph neural networks. In addition, the book introduces the basic concepts and definitions in graph representation learning and discusses the development of advanced graph representation learning methods with a focus on graph neural networks. The book providers researchers and practitioners with an understanding of the fundamental issues as well as a launch point for discussing the latest trends in the science. The authors emphasize several frontier aspects of graph neural networks and utilize graph data to describe pairwise relations for real-world data from many different domains, including social science, chemistry, and biology. Several frontiers of graph neural networks are introduced, which enable readers to acquire the needed techniques of advances in graph neural networks via theoretical models and real-world applications.

Pattern Recognition and Computer Vision

Qingshan Liu,Hanzi Wang,Zhanyu Ma,Weishi Zheng,Hongbin Zha,Xilin Chen,Liang Wang,Rongrong Ji
Pattern Recognition and Computer Vision Book PDF

Author : Qingshan Liu,Hanzi Wang,Zhanyu Ma,Weishi Zheng,Hongbin Zha,Xilin Chen,Liang Wang,Rongrong Ji
Publisher : Springer Nature
Page : 525 pages
File Size : 55,6 Mb
Release : 2023-12-23
Category : Computers
ISBN : 9789819984299

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Pattern Recognition and Computer Vision by Qingshan Liu,Hanzi Wang,Zhanyu Ma,Weishi Zheng,Hongbin Zha,Xilin Chen,Liang Wang,Rongrong Ji Pdf

The 13-volume set LNCS 14425-14437 constitutes the refereed proceedings of the 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023, held in Xiamen, China, during October 13–15, 2023. The 532 full papers presented in these volumes were selected from 1420 submissions. The papers have been organized in the following topical sections: Action Recognition, Multi-Modal Information Processing, 3D Vision and Reconstruction, Character Recognition, Fundamental Theory of Computer Vision, Machine Learning, Vision Problems in Robotics, Autonomous Driving, Pattern Classification and Cluster Analysis, Performance Evaluation and Benchmarks, Remote Sensing Image Interpretation, Biometric Recognition, Face Recognition and Pose Recognition, Structural Pattern Recognition, Computational Photography, Sensing and Display Technology, Video Analysis and Understanding, Vision Applications and Systems, Document Analysis and Recognition, Feature Extraction and Feature Selection, Multimedia Analysis and Reasoning, Optimization and Learning methods, Neural Network and Deep Learning, Low-Level Vision and Image Processing, Object Detection, Tracking and Identification, Medical Image Processing and Analysis.

Neural Information Processing

Teddy Mantoro,Minho Lee,Media Anugerah Ayu,Kok Wai Wong,Achmad Nizar Hidayanto
Neural Information Processing Book PDF

Author : Teddy Mantoro,Minho Lee,Media Anugerah Ayu,Kok Wai Wong,Achmad Nizar Hidayanto
Publisher : Springer Nature
Page : 703 pages
File Size : 41,6 Mb
Release : 2021-12-06
Category : Computers
ISBN : 9783030922702

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Neural Information Processing by Teddy Mantoro,Minho Lee,Media Anugerah Ayu,Kok Wai Wong,Achmad Nizar Hidayanto Pdf

The four-volume proceedings LNCS 13108, 13109, 13110, and 13111 constitutes the proceedings of the 28th International Conference on Neural Information Processing, ICONIP 2021, which was held during December 8-12, 2021. The conference was planned to take place in Bali, Indonesia but changed to an online format due to the COVID-19 pandemic. The total of 226 full papers presented in these proceedings was carefully reviewed and selected from 1093 submissions. The papers were organized in topical sections as follows: Part I: Theory and algorithms; Part II: Theory and algorithms; human centred computing; AI and cybersecurity; Part III: Cognitive neurosciences; reliable, robust, and secure machine learning algorithms; theory and applications of natural computing paradigms; advances in deep and shallow machine learning algorithms for biomedical data and imaging; applications; Part IV: Applications.

Neural Network Tutorials - Herong's Tutorial Examples

Herong Yang
Neural Network Tutorials Herong s Tutorial Examples Book PDF

Author : Herong Yang
Publisher : HerongYang.com
Page : 305 pages
File Size : 43,8 Mb
Release : 2021-03-06
Category : Computers
ISBN : 8210379456XXX

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Neural Network Tutorials - Herong's Tutorial Examples by Herong Yang Pdf

This book is a collection of notes and sample codes written by the author while he was learning Neural Networks in Machine Learning. Topics include Neural Networks (NN) concepts: nodes, layers, activation functions, learning rates, training sets, etc.; deep playground for classical neural networks; building neural networks with Python; walking through Tariq Rashi's 'Make Your Own Neural Network' source code; using 'TensorFlow' and 'PyTorch' machine learning platforms; understanding CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), GNN (Graph Neural Network). Updated in 2023 (Version v1.22) with minor updates. For latest updates and free sample chapters, visit https://www.herongyang.com/Neural-Network.