Machine Learning with Graphs

Instructor: Sungsu Lim @ CNU CSE
Web: CNU e-Learning
​
Course Description: This course teaches key concepts and algorithms for analyzing graphs from the machine learning point of view.
Textbook: Graph Representation Learning by William L. Hamilton

Grading: Assignments 40% (Two Reports), Course Project 60% (Proposal, Draft, Final Report & Presentation)
​Term Project
  • 2~4명 1팀 권장, term paper 최종 보고서 제출 시 팀원 역할 기재
  • Proposal 제출 이후 팀-교수 회의 (대면 or 비대면) 1회 이상 필수
  • Proposal 및 Draft에 대한 관련 분야 전문가 리뷰 및 피드백 제공
  • 한글 작성 권장, Proposal 2쪽 내외, Draft 및 Final Version 8쪽 내외
  • 사이버캠퍼스의 sample paper, candidate topics, 평가 기준 등 참조

Week 01

Introduction and Motivation (March 4, 2021, 1시간 41분) [slides]
  • Chapter 1, Hamilton

Week 02

Background and Traditional Approaches  (March 11, 2021, 2시간 24분) [slides]
  • Chapter 2, Hamilton

Week 03

NetworkX, Snap.py 실습  (March 18, 2021, 1시간 44분) [slides]
  • Practice #1 (NetworkX 기초) [link]
  • Practice #2 (SNAP 활용) [link]
  • Practice #3 (Snap.py 기초) [link]

Week 04

Neighborhood Reconstruction Methods (March 25, 2021, 1시간 37분) [slides]
  • Chapter 3, Hamilton

Week 05

Multi-Relational Data and Knowledge Graphs (April 1, 2021, 1시간 25분) [slides]
  • Chapter 4, Hamilton

Week 06

Foundation of Deep Learning (April 8, 2021, 2시간 1분) [slides]

Week 07

The Graph Neural Network Model (1/2) (April 15, 2021, 1시간 46분) [slides]
  • Chapter 5, Hamilton

Week 08

The Graph Neural Network Model (2/2) & Graph Neural Networks in Practice (April 22, 2021, 1시간 32분) [slides]
  • Chapters 5~6, Hamilton

Week 09

Graph Neural Networks 실습 (April 29, 2021, 1시간 32분) [slides]
  • DGL 한국어 튜토리얼 [link]

Week 10

Proposal Presentation - Zoom (May 6, 2021, 1시간 50분)
  • Project Proposal 제출 및 발표 (Due: May 5, 2021)
  • 총 14개 팀 발표, 10:00~11:50 진행 // 영상 없음

Week 11

Graph Neural Networks 추가 실습 (May 13, 2021, 1시간 22분) [slides]
  • DGL 한국어 튜토리얼 [link]

Week 12

Traditional Graph Generation Approaches (May 20, 2021, 1시간 50분) [slides]
  • Chapter 8, Hamilton

Week 13

Deep Generative Models (May 27, 2021, 1시간 33분) [slides]
  • Chapter 9, Hamilton

Week 14

그래프 기계학습 특강  (June 3, 2021,  40분 + 41분 + 22분 + 22분)
  • 특강 #1: Overlapping Clustering - 임성수 (충남대) [slides]
  • 특강 #2: Graph Summarization - 임성수 (충남대) [slides]
  • 특강 #3: Geosocial Co-Clustering - 김정은 (공주대) [slides]
  • 특강 #4: Cohesive Subgraph Mining - 김정훈 (난양공대) [slides]

Week 15

Final Presentation - Zoom  (June 17, 2021, 2시간 48분) [slides]
  • Term Paper - Draft 제출 (Due: June 7, 2021)
  • Term Paper - Final Report 제출 및 발표 (Due: June 17, 2021)