# From Graph to Knowledge Graph – Algorithms and Applications [Note2]

## Part3. Graph Representation Learning

Problem: structural feature engineering over the graph is expensive.

=> Graph Representation Learning aimed to learn **latent**(潜在的) feature matrix

Representation learning for graph mining?

The goal is to map each node into a latent low-dimensional space such that network structure information is encoded into distributional node representations.

### 1. Skip-gram based graph embedding

#### skip-gram based word embedding

Consider sentence as a node path, each node representing a word.

Two steps:

- random walks over graph to generate node path.
- skip gram model to apply to node path generated in the first step.

**Skip gram with negative sampling**

- For sufficiently large dimension d, the objective of SGNS(Skip-gram with negative sampling) is equivalent to factorizing the PMI matrix(Pointwise mutual information matrix).

### 2. Heterogeneous graph embedding

### 3. Graph convolutional network

Ref: From Graph to Knowledge Graph – Algorithms and Applications