Graph Neural Networks: Models and Applications


Time and Location

Time: 10:30 AM - 12:30 PM, Thursday, April 29, 2021 (UTC-4)
Location: zoom link (TBD)


Graph structured data such as social networks and molecular graphs are ubiquitous in the real world. It is of great research importance to design advanced algorithms for representation learning on graph structured data so that downstream tasks can be facilitated. Graph Neural Networks (GNNs), which generalize the deep neural network models to graph structured data, pave a new way to effectively learn representations for graph-structured data either from the node level or the graph level. Thanks to their strong representation learning capability, GNNs have gained practical significance in various applications ranging from recommendation, natural language processing to healthcare. It has become a hot research topic and attracted increasing attention from the machine learning and data mining community recently. This tutorial of GNNs is timely for SDM 2021 and covers relevant and interesting topics, including representation learning on graph structured data using GNNs, the robustness of GNNs, the scalability of GNNs and applications based on GNNs.

Tutorial Syllabus

  1. Introduction

    1. Graphs and Graph Structured Data

    2. Tasks on Graph Structured Data

    3. Graph neural networks

  2. Foundations

    1. Basic Graph Theory

    2. Graph Fourier Transform

  3. Models

    1. Spectral-based GNN layers

    2. Spatial-based GNN layers

    3. Pooling Schemes for Graph-level Representation Learning

    4. Self-Supervised Learning for GNNs

  4. Applications

    1. Recommendation

Tutorial slides

Tutorial description



Yiqi Wang is a Ph.D. student in the Computer Science and Engineering Department at Michigan State University. She is working on graph neural networks including fundamental algorithms, robustness and applications.

Wei Jin is a Ph.D. student of Computer Science and Engineering at Michigan State University (MSU), supervised by Dr. Jiliang Tang. His interests lie in Graph Representation Learning. Now He works on the area of graph neural network including its theory foundations, model robustness and applications.

Yao Ma is a Ph.D. student of Computer Science and Engineering at Michigan State University. He also works as a research assistant at the Data Science and Engineering lab (DSE lab) led by Dr. Jiliang Tang. His research interests include network embedding and graph neural networks for representation learning on graph-structured data. He has published innovative works in top-tier conferences such as WSDM, ASONAM, ICDM, SDM, WWW, KDD and IJCAI. Before joining Michigan State University, he obtained his master's degree from Eindhoven University of Technology and bachelor's degree from Zhejiang University. Yao will join the Department of Computer Science at New Jersey Institute of Technology as an Assistant Professor in Fall 2021.

Jiliang Tang is an assistant professor in the computer science and engineering department at Michigan State University since Fall@2016. Before that, he was a research scientist in Yahoo Research and got his PhD from Arizona State University in 2015. His research interests including social computing, data mining and machine learning and their applications in education. He was the recipients of 2019 NSF Career Award, the 2015 KDD Best Dissertation runner up and 6 best paper awards (or runner-ups) including WSDM2018 and KDD2016. He serves as conference organizers (e.g., KDD, WSDM and SDM) and journal editors (e.g., TKDD). He has published his research in highly ranked journals and top conference proceedings, which received thousands of citations and extensive media coverage.