AIAA 5028 Machine Learning on Graphs

Introduction

This course covers recent developments in machine learning on graph-structured data. Topics including network embedding, graph neural networks, knowledge graph embedding, generative models for graphs, scalable graph neural networks, explainable graph neural networks, and their applications. You are expected to finish several lab assignments, a survey on graph learning, a course project, and successfully participate a graph competition.

Course materials on Canvas.

Lab Exercise

You are required to finish three coding assignments with Python and Jupyter Notebook. Checkout assignments on Canvas.

• Deadline: please Submit your assignments via Canvas by 11:59 PM GMT+8 for each ddl
• Late submission: 10% penalty of total points for every day an assignment is late

Paper Reading

You are required to submit a survey paper with topics related to graph learning.

• Topic: checkout the following topics or free research (with using graph techniques, avoid too general topics, e.g., deep learning with GNN)
  • Graph-based recommender system
  • Graph-based traffic prediction
  • Domain-specific knowledge graph
  • Graph-based drug discovery
  • TBD
• Final report: 6 pages main text and up to 2 pages references in IJCAI format
• Review: every report will be reviewed by at least three student reviewers
• Grouping: up to 3 team members
• Can be the same topic with course project
• Consider to submit your survey to IJCAI 2023, and good luck!

Course Project

You are required to finish a course project with topics related to graph learning.

• Topic: checkout the following project topics or free research, make sure the novelty of your topic - Graph-based recommender system
  • Graph-based traffic prediction
  • Domain-specific knowledge graph
  • Graph-based drug discovery
  • TBD
• Grouping: No more than 3 students
• Mid-term project proposal: submit the title and abstract of your project
• Project report: 7 pages not including references in IJCAI format, should enclosed with code
• Final project presentation: in the end of semester
• Consider to submit to IJCAI/ACL/ICML/KDD/SIGIR, and good luck!

Graph Competition

Finally, you are required to participate a open-world competition with using graph learning techniques.

• Competition: team up for a data science competition, e.g.,
  • Heterogeneous Graph Benchmark - Node Classification
  • Knowledge-aware Recommendation
  • other competitions on Kaggle
• Grouping: no more than 3 students
• Grading formula: (100%-percentage your rank)/10%
• Basic requirements:
  • Using graph learning techniques in the solution
  • Submit a brief report with code
  • A competition with no less than 50 teams