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Relational Data With Graph Convolutional Networks

Found 4 free book(s)
JOURNAL OF LA A Comprehensive Survey on Graph Neural …

JOURNAL OF LA A Comprehensive Survey on Graph Neural

arxiv.org

Index Terms—Deep Learning, graph neural networks, graph convolutional networks, graph representation learning, graph autoencoder, network embedding I. INTRODUCTION T HE recent success of neural networks has boosted re-search on pattern recognition and data mining. Many machine learning tasks such as object detection [1], [2],

  Network, Data, Survey, Comprehensive, Graph, Neural, Convolutional, A comprehensive survey on graph neural, Graph convolutional networks

2 Related Work

2 Related Work

arxiv.org

cently, graph neural networks (GNNs) (Defferrard, Bresson, and Vandergheynst 2016) have shown success in modelling graph-structured data. These include graph convolution net-works (GCNs) (Kipf and Welling 2016), graph attention net-works (GATs) (Veliˇckovi ´c et al. 2017) and multi-relational approaches (Schlichtkrull et al. 2018). However ...

  Network, Data, Work, Relational, Graph, Net work

Graph Representation Learning - McGill University School ...

Graph Representation Learning - McGill University School ...

www.cs.mcgill.ca

of graph-structured data and graph properties are relatively self-contained. However, the book does assume a background in machine learning and a familiarity with modern deep learning methods (e.g., convolutional and re-current neural networks). Generally, the book assumes a level of machine

  Network, Data, Graph, Convolutional

Natural Language Processing

Natural Language Processing

raw.githubusercontent.com

CONTENTS 3 4.5.1 Metadata as labels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 4.5.2 Labeling data ...

  Data

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