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Network Embedding, Yang, Cheng Liu, Zhiyuan Tu, Cunchao Shi, Chuan Sun, Maosong


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Автор: Yang, Cheng Liu, Zhiyuan Tu, Cunchao Shi, Chuan Sun, Maosong
Название:  Network Embedding
ISBN: 9783031004629
Издательство: Springer
Классификация:


ISBN-10: 3031004620
Обложка/Формат: Paperback
Страницы: 220
Вес: 0.47 кг.
Дата издания: 23.03.2021
Серия: Synthesis lectures on artificial intelligence and machine learning
Язык: English
Иллюстрации: Xxi, 220 p.
Размер: 235 x 191
Читательская аудитория: Professional & vocational
Подзаголовок: Theories, methods, and applications
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: 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.


Embeddings in Natural Language Processing

Автор: Pilehvar, Mohammad Taher Camacho-Collados, Jose
Название: Embeddings in Natural Language Processing
ISBN: 3031010493 ISBN-13(EAN): 9783031010491
Издательство: Springer
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Цена: 7685.00 р.
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Описание: Embeddings have undoubtedly been one of the most influential research areas in Natural Language Processing (NLP). The book starts by explaining conventional word vector space models and word embeddings (e.g., Word2Vec and GloVe) and then moves to other types of embeddings, such as word sense, sentence and document, and graph embeddings.

Embeddings in natural language processing

Автор: Pilehvar, Mohammad Taher Camacho-collados, Jose
Название: Embeddings in natural language processing
ISBN: 1636390218 ISBN-13(EAN): 9781636390215
Издательство: Mare Nostrum (Eurospan)
Рейтинг:
Цена: 9286.00 р.
Наличие на складе: Нет в наличии.

Описание: Provides a high-level synthesis of the main embedding techniques in NLP, in the broad sense. The book starts by explaining conventional word vector space models and word embeddings (e.g., Word2Vec and GloVe) and then moves to other types of embeddings, such as word sense, sentence and document, and graph embeddings.

Network Embedding: Theories, Methods, and Applications

Автор: Yang Cheng, Liu Zhiyuan, Tu Cunchao
Название: Network Embedding: Theories, Methods, and Applications
ISBN: 1636390447 ISBN-13(EAN): 9781636390444
Издательство: Mare Nostrum (Eurospan)
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Цена: 12751.00 р.
Наличие на складе: Нет в наличии.

Описание:

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.

This book provides a comprehensive introduction to the basic concepts, models, and applications of network representation learning (NRL). The book starts with an introduction to the background and rising of network embeddings as a general overview for readers. Then 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.

Network Embedding: Theories, Methods, and Applications

Автор: Yang Cheng, Liu Zhiyuan, Tu Cunchao
Название: Network Embedding: Theories, Methods, and Applications
ISBN: 1636390463 ISBN-13(EAN): 9781636390468
Издательство: Mare Nostrum (Eurospan)
Рейтинг:
Цена: 15939.00 р.
Наличие на складе: Нет в наличии.

Описание:

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.

This book provides a comprehensive introduction to the basic concepts, models, and applications of network representation learning (NRL). The book starts with an introduction to the background and rising of network embeddings as a general overview for readers. Then 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.

Qos-Aware Virtual Network Embedding

Автор: Jiang Chunxiao, Zhang Peiying
Название: Qos-Aware Virtual Network Embedding
ISBN: 9811652201 ISBN-13(EAN): 9789811652202
Издательство: Springer
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Цена: 20962.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Therefore, network resources need to be reasonably allocated according to users` QoS requirements to avoid the waste of network resources.In this book, based on the analysis of the principle of VNE algorithm, we provide a VNE scheme for users with differentiated QoS requirements.

Graph Embedding for Pattern Analysis

Автор: Yun Fu; Yunqian Ma
Название: Graph Embedding for Pattern Analysis
ISBN: 1489990623 ISBN-13(EAN): 9781489990624
Издательство: Springer
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Цена: 16977.00 р.
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Описание: This book presents advances in graph embedding theories, such as nonlinear manifold graph, linearization method, graph based subspace analysis, L1 graph, hypergraph, undirected graph and graph in vector spaces, and describes their real-world applications.

Embedding Knowledge Graphs with RDF2vec

Автор: Paulheim
Название: Embedding Knowledge Graphs with RDF2vec
ISBN: 3031303865 ISBN-13(EAN): 9783031303869
Издательство: Springer
Рейтинг:
Цена: 6288.00 р.
Наличие на складе: Поставка под заказ.

Описание: This book explains the ideas behind one of the most well-known methods for knowledge graph embedding of transformations to compute vector representations from a graph, known as RDF2vec. The authors describe its usage in practice, from reusing pre-trained knowledge graph embeddings to training tailored vectors for a knowledge graph at hand. They also demonstrate different extensions of RDF2vec and how they affect not only the downstream performance, but also the expressivity of the resulting vector representation, and analyze the resulting vector spaces and the semantic properties they encode.

Minimum-Distortion Embedding

Автор: Akshay Agrawal, Alnur Ali, Stephen Boyd
Название: Minimum-Distortion Embedding
ISBN: 1680838881 ISBN-13(EAN): 9781680838886
Издательство: Mare Nostrum (Eurospan)
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Цена: 13721.00 р.
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Описание: Presents a general framework for faithful embedding called minimum-distortion embedding (MDE) that generalizes the common cases in which similarities between items are described by weights or distances. The MDE framework is simple but general.

Kernel mean embedding of distributions:

Автор: Muandet, Krikamol Fukumizu, Kenji Sriperumbudur, Bharath Scholkopf, Bernhard
Название: Kernel mean embedding of distributions:
ISBN: 1680832883 ISBN-13(EAN): 9781680832884
Издательство: Неизвестно
Рейтинг:
Цена: 13656.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: This monograph provides a comprehensive review of kernel mean embeddings of distributions and, in the course of doing so, discusses some challenging issues that could potentially lead to new research directions. The targeted audience includes graduate students and researchers in machine learning and statistics who are interested in the theory and applications of kernel mean embeddings.

Machine Learning in Social Networks: Embedding Nodes, Edges, Communities, and Graphs

Автор: Aggarwal Manasvi, Murty M. N.
Название: Machine Learning in Social Networks: Embedding Nodes, Edges, Communities, and Graphs
ISBN: 9813340215 ISBN-13(EAN): 9789813340213
Издательство: Springer
Цена: 9083.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание:

Introduction

1.1 introduction

1.2 Notations used in Book

1.3 Contents covered in this book

2 Representations of Networks

2.1 Introduction

2.2 Networks Represented as Graphs

2.3 Data Structures to Represent Graphs

2.3.1 Matrix Representation

2.3.2 Adjacency List

2.4 Network Embeddings

2.5 Evaluation Datasets

2.5.1 Evaluation Datasets

2.5.2 Evaluation Metrics

2.6 Machine Learning Downstream Tasks

2.6.1 Classification

2.6.2 Clustering

2.6.3 Link Prediction (LP)

2.6.4 Visualization

2.6.5 Network Reconstruction

2.7 Embeddings based on Matrix Factorization

2.7.1 Singular Value Decomposition (SVD)

2.7.2 Matrix Factorization based Clustering

2.7.3 Soft Clustering as Matrix Factorization

2.7.4 Non-negative Matrix factorization (NMF)

2.8 Word2vec

2.8.1 Skipgram model

2.9 Learning Network Embeddings

2.9.1 Supervised Learning

2.9.2 Unsupervised Learning

2.9.3 Node and Edge Embeddings

2.9.4 Graph Embedding

2.10 Summary

3 Deep Learning

3.1 Introduction

3.2 Neural Networks

3.2.1 Perceptron

3.2.2 Characteristics of Neural Networks

3.2.3 Multilayer Perceptron Networks

3.2.4 Training MLP Networks

3.3 Convolution Neural Networks

3.3.1 Activation Function

3.3.2 Initialization of Weights

3.3.3 Deep Feedforward Neural Network

3.4 Recurrent Networks

3.4.1 Recurrent Neural Networks

3.4.2 Long Short Term Memory

3.4.3 Different Gates used by LSTM

3.4.4 Training of LSTM Models

3.5 Learning Representations using Autoencoders

3.5.1 Types of Autoencoders

3.6 Summary

References

4 Embedding Nodes and Edge

4.1 Introduction

4.2 Representation of Node and Edges as Vectors

4.3 Embeddings based on Random Walks

4.4 Embeddings based on Matrix Factorization

4.5 Graph Neural Network Models

4.6 State of the art algorithms

4.7 Evaluation methods and Machine Learning tasks

4.8 Summary

References

5 Embedding Graphs

5.1 Introduction

5.2 Representation of Graphs as Vectors

5.3 Graph Representation using Node Embeddings

5.4 Graph Pooling Techniques

5.4.1 Global Pooling Methods

5.4.2 Hierarchical Pooling Methods

5.5 State of the art algorithms

5.6 Evaluation methods and Machine Learning tasks

5.7 Summary

References
Embeddings in natural language processing

Автор: Pilehvar, Mohammad Taher Camacho-collados, Jose
Название: Embeddings in natural language processing
ISBN: 1636390234 ISBN-13(EAN): 9781636390239
Издательство: Mare Nostrum (Eurospan)
Рейтинг:
Цена: 12058.00 р.
Наличие на складе: Нет в наличии.

Описание: Provides a high-level synthesis of the main embedding techniques in NLP, in the broad sense. The book starts by explaining conventional word vector space models and word embeddings (e.g., Word2Vec and GloVe) and then moves to other types of embeddings, such as word sense, sentence and document, and graph embeddings.

Cross-Lingual Word Embeddings

Автор: Sogaard, Anders Vulic, Ivan Ruder, Sebastian Faruqui, Manaal
Название: Cross-Lingual Word Embeddings
ISBN: 3031010434 ISBN-13(EAN): 9783031010439
Издательство: Springer
Рейтинг:
Цена: 6986.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.


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