# Graph Embedding学习笔记（3）：Graph Convolution Networks

## 笔记

CNN目前主流的应用场景都是规则的空间结构数据，比如图像是2D grids，语音和文本是1D grids。

### Patchy-SAN

Neighborhood Normalization是这篇文章的关键，上图是作者对这个步骤的讲解，具体含义还没有搞明白。

### GCN

For these models, the goal is then to learn a function of signals/features on a graph G=(V,E) which takes as input:

• A feature description xi for every node i; summarized in a N×D feature matrix X (N: number of nodes, D: number of input features)
• A representative description of the graph structure in matrix form; typically in the form of an adjacency matrix A (or some function thereof)

and produces a node-level output Z (an N×F feature matrix, where F is the number of output features per node). Graph-level outputs can be modeled by introducing some form of pooling operation

## 附录

Graph Convolution Networks

SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS

Learning Convolutional Neural Networks for Graphs

Patchy-SAN讲义

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HarryZhu · 2018年09月30日