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首页GCN大佬Thomas Kipf博士论文《深度学习图结构表示》

GCN大佬Thomas Kipf博士论文《深度学习图结构表示》

《深度学习图结构表示》

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Thomas Kipf是阿姆斯特丹大学博士生，是GCN作者。最近他毕业博士论文公布了，《深度学习图结构表示》178页pdf阐述图卷积神经网络等机制与应用，包括作者一系列原创图深度学习工作，有GCN、GAE等，是研究该领域不可缺少的阅读文献。

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Deep learning with graph-structured representations

Kipf, T.N.

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Citation for published version (APA):

Kipf, T. N. (2020). Deep learning with graph-structured representations.

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Download date: 07 2020

Deep Learning with

Graph-Structured

Representations

Deep Learning with Graph-Structured Representations Thomas Kipf

Thomas Kipf

Deep Learning with

Graph-Structured Representations

ACADEMISCH PROEFSCHRIFT

ter verkrijging van de graad van doctor

aan de Universiteit van Amsterdam

op gezag van de Rector Magniﬁcus

prof. dr. ir. K.I.J. Maex

ten overstaan van een door het College voor Promoties

ingestelde commissie,

in het openbaar te verdedigen

op donderdag 23 april 2020, te 14.00 uur

door

Thomas Norbert Kipf

geboren te Roth

P R O M O T I E C O M M I S S I E

Promotor:

prof. dr. M. Welling Universiteit van Amsterdam

Copromotor:

dr. I.A. Titov University of Edinburgh

Overige leden:

prof. dr. M.M. Bronstein Imperial College London

prof. dr. F.A.H. van Harmelen Vrije Universiteit Amsterdam

prof. dr. M. de Rijke Universiteit van Amsterdam

dr. P.W. Battaglia DeepMind Technologies Ltd

dr. H.C. van Hoof Universiteit van Amsterdam

Faculteit der Natuurwetenschappen, Wiskunde en Informatica

The work described in this thesis has been primarily carried out at the Ams-

terdam Machine Learning Lab (AMLab) of the University of Amsterdam and

in part during an internship at DeepMind Technologies Ltd, London, UK. The

research carried out at the University of Amsterdam was funded by SAP SE.

Printed by Ridderprint, The Netherlands.

ISBN: 978-94-6375-851-2

 2020 by T. N. Kipf, Amsterdam, The Netherlands.

S U M M A R Y

In this thesis, Deep Learning with Graph-Structured Representations, we propose

novel approaches to machine learning with structured data. Our proposed

methods are largely based on the theme of structuring the representations and

computations of neural network-based models in the form of a graph, which

allows for improved generalization when learning from data with both explicit

and implicit modular structure.

Our contributions are as follows:

• We propose graph convolutional networks (GCNs) (Kipf and Welling,

2017; Chapter 3) for semi-supervised classiﬁcation of nodes in graph-

structured data. GCNs are a form of graph neural network that per-

form parameterized message-passing operations in a graph, modeled as a

ﬁrst-order approximation to spectral graph convolutions. GCNs achieved

state-of-the-art performance in node-level classiﬁcation tasks in a number

of undirected graph datasets at the time of publication.

• We propose graph auto-encoders (GAEs) (Kipf and Welling, 2016; Chap-

ter 4) for unsupervised learning and link prediction in graph-structured

data. GAEs utilize an encoder based on graph neural networks and a

decoder that reconstructs links in a graph based on a pairwise scoring

function. We further propose a model variant framed as a probabilistic

generative model that is trained using variational inference, which we

name variational GAE. GAEs and variational GAEs are particularly suited

for representation learning on graphs in the absence of node labels.

• We propose relational GCNs (Schlichtkrull and Kipf et al., 2018; Chapter

5) that extend the GCN model to directed, relational graphs with multi-

ple edge types. Relational GCNs are well-suited for modeling relational

data and we demonstrate an application to semi-supervised entity classi-

ﬁcation in knowledge bases.

iii

剩余177页未读，继续阅读

HashGust

2020-06-01

大神论文，学习入门

gcn：在TensorFlow中实现图卷积网络

图卷积网络这是图卷积网络的TensorFlow实现，用于图中节点的（半监督）分类任务，如我们的论文所述： Thomas N.Kipf，Max Welling，（ICLR 2017）有关高级解释，请查看我们的博客文章：托马斯·基普夫（Thomas Kipf），（2016）安装 python setup.py install 要求张量流（> 0.12）网络运行演示 cd gcn python train.py 数据为了使用您自己的数据，您必须提供 N×N邻接矩阵（N是节点数）， N×D特征矩阵（D是每个节点的特征数），以及 N by E二进制标签矩阵（E是类数）。看一看在load_data()函数utils.py为例。在此示例中，我们加载引文网络数据（Cora，Citeseer或Pubmed）。原始数据集可以在以下位置找到： : 。在我们的版本中（请参

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