KDD2021 | 最新GNN官方教程

介绍

KDD是数据挖掘类顶级学术会议，也是CCF-A类会议。本文整理了KDD2021上关于Graph，Graph Neural Network， Graph Representation Learning的教程，顶级学者的在线教学，值得一听~

1. Graph Representation Learning:Foundations, Methods, Applications and Systems
2. Deep Learning on Graphs for Natural Language Processing
3. Automated Machine Learning on Graph
4. OGB Large-Scale Challenge (OGB-LSC)
5. Heterogeneous Information Network Analysis and Applications
6. Heterogenous Graph Deep Learning and Applications
7. Learning on Graphs: Methods and Applications

教程详细安排见：https://kdd.org/kdd2021/files/sponsors/KDD_Full_Program.pdf

Graph Representation Learning:Foundations, Methods, Applications and Systems

Time and Location

Time: Aug 14: 9 am - 12 pm, 1 pm - 4 pm (Singapore Time)

Zoom Link: Please use the link on KDD virtual platform

Abstract

Graphs such as social networks and molecular graphs are ubiquitous data structures in the real world. Due to their prevalence, it is of great research importance to extract meaningful patterns from graph structured data so that downstream tasks can be facilitated. Instead of designing hand-engineered features, graph representation learning has emerged to learn representations that can encode the abundant information about the graph. It has achieved tremendous success in various tasks such as node classification, link prediction, and graph classification and has attracted increasing attention in recent years. In this tutorial, we systematically review the foundations, techniques, applications and advances in graph representation learning.

Tutorial Syllabus

The topics of this full-day include (but are not limited to) the following:

1. Graph Theory and Graph Fourier Analysis
2. Basic Graph Neural Networks
3. CogDL Toolkit for Graph Neural Networks
4. Scalable Graph Neural Networks
5. Network Embedding Theories and Systems
6. Heterogeneous Graph Neural Networks