Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 14.1 - Generative Models for Graphs

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: stanford.io/3jO8OsE
Jure Leskovec
Computer Science, PhD
In this lecture, we will cover generative models for graphs. The goal of generative models for graphs is to generate synthetic graphs which are similar to given example graphs. Graph generation is important as it can offer insight on the formulation process of graphs, which is crucial for predictions, simulations and anomaly detections on graphs. In the first part, we will introduce the properties of real-world graphs, where a successful graph generative model should fit these properties. These graph statistics include degree distribution, clustering coefficient, connected components and path length.
To follow along with the course schedule and syllabus, visit:
web.stanford.edu/class/cs224w/
0:00 Introduction
0:27 Motivation for Graph Generation
0:52 The Problem: Graph Generation
1:14 Why Do We Study Graph Generation
2:37 Road Map of Graph Generation
4:09 Plan: Key Network Properties
5:29 Clustering Coefficient
7:24 Connectivity
9:39 Case Study: MSN Graph
10:47 Communication Network
15:55 Connected Components
17:01 Path Length
19:15 MSN: Key Network Properties
#machinelearning #machinelearningcourse

[@doyleBellamy03]

Explaining your own paper and studying for a lecture must be a very pleasant experience. Inspiring lecture, thank you😃