<p>This is a comprehensive introduction to the basic concepts, models, and applications of network representation learning (NRL) and the background and rise of network embeddings (NE).<br /> <br /> It introduces the development of NE techniques by presenting several representative methods on general graphs, as well as a unified NE framework based on matrix factorization. Afterward, it presents the variants of NE with additional information: NE for graphs with node attributes/contents/labels; and the variants with different characteristics: NE for community-structured/large-scale/heterogeneous graphs. Further, the book introduces different applications of NE such as recommendation and information diffusion prediction. Finally, the book concludes the methods and applications and looks forward to the future directions.<br /> <br /> Many machine learning algorithms require real-valued feature vectors of data instances as inputs. By projecting data into vector spaces, representation learning techniques have achieved promising performance in many areas such as computer vision and natural language processing. There is also a need to learn representations for discrete relational data, namely networks or graphs. Network Embedding (NE) aims at learning vector representations for each node or vertex in a network to encode the topologic structure. Due to its convincing performance and efficiency, NE has been widely applied in many network applications such as node classification and link prediction.</p>

Network Embedding: Theories, Methods, and Applications (Synthesis Lectures on Artificial Intelligence and Machine Learning, 48)

Computers & Technology

Liu, Zhiyuan

Sun, Maosong

Shi, Chuan

Yang, Cheng

Tu, Cunchao

Computer Science

Morgan Claypool

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

<p>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.</p>

Zhou, Jie

<p><p><b>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.</b> 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.</p> 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.</p>

<p><p>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.</p> 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.</p>

Springer

Representation Learning for Natural Language Processing

<p><p>This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions.</p> The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.</p>

Lin, Yankai

Switching Arc Phenomena in Transmission Voltage Level Vacuum Circuit Breakers

<p><div>Vacuum circuit breakers are widely used in distribution power systems for their advantages such as maintenance free and eco-friendly. Nowadays, most circuit breakers used at transmission voltage level are SF6 circuit breakers, but the SF6 they emit is one of the six greenhouse gases defined in Kyoto Protocol. Therefore, the development of transmission voltage level vacuum circuit breaker can help the environment. The switching arc phenomena in transmission voltage level vacuum circuit breakers are key issues to explore.&nbsp;</div> <div><br /></div> <div>This book focuses on the high-current vacuum arcs phenomena at transmission voltage level, especially on the anode spot phenomena, which significantly influence the success or failure of the short circuit current interruption. Then, it addresses the dielectric recovery property in current interruption. Next it explains how to determine the closing/opening displacement curve of transmission voltage level vacuum circuit breakers based on the vacuum arc phenomena. After that, it explains how to determine key design parameters for vacuum interrupters and vacuum circuit breakers at transmission voltage level. At the end, the most challenging issue for vacuum circuit breakers, capacitive switching in vacuum, is addressed.&nbsp;</div> <div><br /></div> <div>The contents of this book will benefit researchers and engineers in the field of power engineering, especially in the field of power circuit breakers and power switching technology.&nbsp;</div></p>

Engineering & Transportation

Wang, Jianhua

Geng, Yingsan

Wang, Zhenxing

Engineering

See the best price to sell, buy, or rent books by Liu, Zhiyuan. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Liu, Zhiyuan

See the best price to sell, buy, or rent books by Liu, Zhiyuan. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Network Embedding: Theories, Methods, and Applications (Synthesis Lectures on Artificial Intelligence and Machine Learning, 48)

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)

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

Representation Learning for Natural Language Processing

Representation Learning for Natural Language Processing

Switching Arc Phenomena in Transmission Voltage Level Vacuum Circuit Breakers

Switching Arc Phenomena in Transmission Voltage Level Vacuum Circuit Breakers