KDD'16 paper, Standford

# Brief Introduction

- For now, traditional & typical node embedding technique
- Propose graph feature learning approaches
- Inspired by paper "word2vec"
- Adopting word embedding from "skip-diagram" idea to nodes of graph
- Propose biased walking technique

# Background

## Problems in Graph

- Node classification (node-level task)
- Node degree, centrality, clustering coefficient, graphlet

- Link prediction (link-level task)
- Distance, local neighborhood, global neighborhood

- Graph classification (graph-level task)

## Node Embedding in Graph

- A mapping of nodes to a low-dimensional space of features

Traditionals → node embeddings → GNNs → Knowledge graphs → Deep Generative graph models

## Conventional method (Linear algebraic approach)

### PCA (Principal Component Analysis)

주성분 분석(PCA)

## SVM (Support Vector Machine)

[Data Mining] Support Vector Machine

## Shallow encoding