[论文精读]Graph Attention Networks

论文精读]Graph Attention Networks"/>

[论文精读]Graph Attention Networks

论文原文：[1710.10903] Graph Attention Networks (arxiv)

英文是纯手打的！论文原文的summarizing and paraphrasing。可能会出现难以避免的拼写错误和语法错误，若有发现欢迎评论指正！文章偏向于笔记，谨慎食用！

1. 省流版

1.1. 心得

（1）Intro里面就包含了related work的样子？

1.2. 论文框架图

2. 论文逐段精读

2.1. Abstract

        ①They proposed a graph attention networks (GATs), which is both suitable for inductive and transductive problems

        ②There is no need for special and costly matrix operation

        ③They test their model in Cora, Citeseer, Pubmed citation network datasets and proteinprotein interaction dataset

upfront  adj.预付的;坦率的;诚实的;直爽的;预交的  adv.预付地，先期支付地

2.2. Introduction

        ①CNN has been widely used in translation, image classification, semantic segmentation. However, it can not be used in none-grid, i.e. irregular representation, such as social/telecommunication/biological networks, 3D meshes, brain connectomes. Thus, graph structure can describe these structures more accurately

        ②Early works adopted recursive neural networks to process directed acyclic graphs

        ③They introduced spectral and non-spectral methods of graph processing

        ④Allowing different sizes of input, attention mechanism has been sucessfully used in NLP

        ⑤Attention mechanism is able to parallelize neighbors, assign weights to neighbors and be used in inductive learning

acyclic  adj.无环的;非循环的;非周期的;非环状的

reminiscent  adj.怀旧的;使回忆起(人或事);回忆过去的;缅怀往事的  n.回忆者;追记前事者

2.3. GAT architecture

2.3.1. Graph attention layer

2.3.2. Comparisons to related 

2.4. Evaluation

2.4.1. Datasets

2.4.2. State-of-the-art method

2.4.3. Experimental setup

2.4.4. Results

2.5. Conclusions

3. 知识补充

3.1. Spectral and non-spectral approaches for GNN

3.2. Spectral domain and frequency domain

（1）Spectral domain: mainly used in GNN, adopting Fourier transform on space dimensionality

（2）Frequency domain: mainly used in signal and image processing, adopting Fourier transform on temporal dimensionality

4. Reference List

Velickovic, P. et al. (2018) 'Graph Attention Networks', ICLR 2018. doi: .48550/arXiv.1710.10903