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.

Target Audience

The topics of this tutorial cover main research directions of network embedding, graph neural network and deep learning; and the target audiences are those who are interested in graph representation learning and deep learning from both academia and industry.

Tutorial Syllabus

The topics of this full-day tutorial 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

Tutorial slides

Part 1 (Click Here)

1. Graph theory and Graph Fourier Analysis

2. Foundations of Graph Neural Networks

Part 2 (Click Here)

1. CogDL Toolkit for Graph Neural Networks

2. Scalable Graph Neural Networks

Part 3 (Click Here)

1. Network embedding theories and systems

Part 4 (Click Here)

1. Heterogeneous Graph Neural Networks

Presenters

Wei Jin
Michigan State University, USA

Yao Ma
New Jersey Institute of Technology, USA

Yiqi Wang
Michigan State University, USA

Xiaorui Liu
Michigan State University, USA

Jiliang Tang
Michigan State University, USA

Yukuo Cen
Tsinghua University, China

Jiezhong Qiu
Tsinghua University, China

Jie Tang
Tsinghua University, China

Chuan Shi
Beijing University of Posts and Telecommunications, China

Yanfang Ye
Case Western Reserve University, USA

Jiawei Zhang
Florida State University, USA

Philip S. Yu
University of Illinois at Chicago, USA