# Overview of Graph Representation Learning

* How to apply power of deep learning to more complex domains? 
* Graphs are the new frontier of deep learning (connect things) 
  * GNN: hottest subfield in ML 
    * Flexibility to model complex data 
* Graphs 
  * Molecules, knowledge graph, software
  * Applications: recommender system, neutrino detection, LHC, fake news detection, drug repurposing, chemistry, computer graphics, VR, robotics, autonomous driving, medicine 
* Stanford: deployed technology 
* Workshop: representation learning for Graph 

Discuss

* Tools and frameworks 
* Short talks about a wide range of application domains 
  * Graphics and Vision
  * Fraud and intrusion detection 
  * Financial networks 
  * Knowledge Graph and Reasoning 
  * ...
* Software tools 

Goal: Representation Learning 

* Map nodes to d-dim embedding such that similar nodes in the network are embedded close together 
  * Feature representation, embedding 
* DL in graphs 
  * Input: network 
  * Predictions: node labels, new links, generated graphs and subgraphs 
* Why hard 
  * Networks are complex 
    * Arbitrary size and complex topological structure (no spatial locality like grids)
    * No fixed node ordering or reference point 
    * Often dynamic 
* Problem setup 
  * Graph G, vertex set V, adjacency matrix A, node features X 
* Key idea: network is a computation graph 
  * Learn how to represent through the network 
  * Each node defines a computation graph 
  * Train on a set of nodes, i.e. a batch of computation graphs 
    * Back prop through the computation graphs 
    * Scale (small # of parameters) 
* Benefits 
  * No manual feature engineering is needed 
  * End-to-end learning results in optimal features 
  * Any graph ML task 
    * Node-level, link-level
  * Scalable 
* Key idea
  * GNN adapts to the shape of the data 
    * Other DL assume fixed input 
    * GNN makes no such assumptions