Relational Data With Graph Convolutional Networks
Found 4 free book(s)JOURNAL OF LA A Comprehensive Survey on Graph Neural …
arxiv.orgIndex 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],
2 Related Work
arxiv.orgcently, 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 ...
Graph Representation Learning - McGill University School ...
www.cs.mcgill.caof 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
Natural Language Processing
raw.githubusercontent.comCONTENTS 3 4.5.1 Metadata as labels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 4.5.2 Labeling data ...