# KDD Tutorial 2021

# Graph Representation Learning:Foundations, Methods, Applications and Systems

# Time and Location

Time: TBD Zoom Link: TBD

# 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. Foundations of Graph Neural Networks
  3. Network embedding theories and systems
  4. Scalable Graph Neural Networks
  5. CogDL Toolkit for Graph Neural Networks
  6. Heterogeneous Graph Neural Networks

# Tutorial slides


# Presenters:

  • Wei Jin, Michigan State University, USA
  • Yao Ma, Michigan State University, USA
  • Yiqi Wang, Michigan State University, USA
  • Xiaorui Liu, Michigan State University, USA
  • Jiliang Tang, Michigan State University, USA
  • Yukuo Cen, Tsinghua University, China
  • Jie Zhong, 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

# Acknowledgement

Thanks to the support of CogDL Team (opens new window).

最后更新于: 2021/6/7 下午3:30:53