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
