### Article Source

# Large-scale Graph Representation Learning

- Institute for Pure and Applied Mathematics, UCLA
- February 7, 2018

## New Deep Learning Techniques 2018

- “Large-scale Graph Representation Learning”
- Jure Leskovec, Stanford University

## Abstract

*Machine learning on graphs* is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. The primary challenge in this domain is **finding a way to represent, or encode, graph structure** so that it can be easily exploited by machine learning models. However, traditionally machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph.
In this talk I will discuss methods that *automatically learn to encode graph structure* into *low-dimensional embeddings*, using techniques based on deep learning and nonlinear dimensionality reduction. I will provide a conceptual review of key advancements in this area of representation learning on graphs, including *random-walk based* algorithms, and *graph convolutional networks*.