DSAA6000B

Introduction to Graph Learning (Spring 2023)

Instructor: LI,Jia

TA: Gao, Ziqi

Time: We, Fr 4:30 PM - 5:50PM

Venue: E1-150

Introduction

Graph, as a very expressive model, has been widely used to model real-world entities and their relationships in application-specific networks. In this course, students will gain a thorough introduction to the basics of graph theories, as well as cutting-edge research in deep learning for graphs. The topics include graph embeddings, graph neural networks, graph clustering models, graph generative models, adversarial attacks on graphs, graph reasoning, etc.

Prerequisites

Linear Algebra (Strong)

Data Structure and Algorithms (Medium)

Probability theory (Medium) CS229

Announcements

April . 14  Next Wes (April. 19), there is no lecture due to business trip. To make up, we shall extend our lecture today (April. 14) and April. 21 to 4:30PM-6:30PM.

Mar . 24  Next Wes (Mar. 29), TA will give a tutorial about assignment and paper reading, the detailed requirement is here. 

Mar . 17  we don't have lectures on Mar. 22. Instead, we invite Dr. Yu Rong to give a talk about geometric GNN at E1-101 1:30pm exactly that day.  

Feb . 7   hi all. The first class starts at 4:30 pm, Feb. 8. See you.  

Grading 

Paper reading: 30% (must choose from the paper list, upload your paper reading video to Canvas. Due: Apr 22 Beijing Time)

Assignment: 20% (release: late Mar.  Due: Apr 15)

Project: 50%

Reference and Handouts

Reference books and courses for extra reading:

[1] CS22W: Machine Learning with Graphs. Jure Leskovec.

[2] Computation  & Machine Learning on Graphs. Pan Li. 

[3] Graph Representation Learning. William l. Hamilton. 

• Handout 1: Introduction 

• Handout 2: Graph Embedding 

• Handout 3: Community Detection

• Handout 4: Semi-Supervised Learning

• Handout 5: Graph Neural Networks 1 [Tutorial]

• Handout 6: Graph Neural Networks 2

• Handout 7: GNN Training

• Handout 8: Spatial-temporal GNN

• Handout 9: Link Prediction

• Handout 10: Graph-level Representation

• Handout 11: Graph Self-supervised Learning

• Handout 12: Knowledge Graph and ChatGPT

Paper Reading Schedule

The schedule is here: google doc.  Book the topic (chosen from the following paper pool) as early as possible. Everyone's topic is unique. First come first serve!

Paper List

[1] DeepWalk.  Brian Perozzi et al. KDD'14.

[2] node2vec. Aditya Grover et al. KDD'16. 

[3] GCN. Thomas N. Kipf et al. ICLR'17. 

[4] GAT.  Petar Veličković et al. ICLR'18. 

[5] GIN. Keyulu Xu et al. ICLR'19. 

[6] SGC. Felix Wu et al. ICML'19.

[7] DGI.  Petar Veličković et al. ICLR'19.

[8] GMI. Zhen Peng et al. WWW'20. 

[9] DCRNN. Yaguang Li et al. ICLR'18.

[10] STGCN.  Bing Yu et al. IJCAI'18.

[11] Nettack. Daniel Zügner et al. KDD'18.

[12] CD-ATTACK. Jia Li et al. WWW'20. 

[13] DiffPool.  Zhitao Ying et al. NeurIPS'18. 

[14] MinCutPool. Filippo Maria Bianchi et al. ICML'20.

[15] GraphRNN.  Jiaxuan You et al. ICML'18. 

[16] JT-VAE. Wengong Jin et al. ICML'18.

[17] GCMC. Rianne van den Berg et al. KDD'18. 

[18] PinSAGE.  Zhitao Ying et al. KDD'18.

[19] TransE. Antoine Bordes et al. NeurIPS'13.

[20] GMN.  Yujia Li et al. ICML'19.

[21] SEAL. Muhan Zhang et al. NeurIPS'18. 

[22] LDS. Luca Franceschi et al. ICML'19.

Project

Each one chooses a research topic related to course material, e.g., graph learning. The report should follow ACM format with strict 6 pages limitation, including reference and appendix, see the following for reference https://kdd.org/kdd2021/calls/view/call-for-research-track-papers.  Here are some tips:

1. The report should at least consist of introduction, related work, methodology and experiment. Theoretical deviation is not a necessity but encouraged.

2. Use concise and clear language.

3. Clearly declare your difference with previous works.

4. If there is any theoretical deviation, check your assumption and make sure it is non-fragile. 

The ddl for the final report is May 25th Beijing time, to the email of TA ( zgaoat@connect.ust.hk ).

Resources

Understanding Matrix

Chinese Blog

Machine Learning

A few useful things to know about machine learning

Software

DGL (an open-source library for deep learning on graphs) 

Graph learning tutorial

Advanced deep graph learning: deeper, faster, robuster and unsupervised (WWW'21 tutorial)

Graph learning publications

Graph based deep learning literature