Welcome to SSIE 641 Advanced Topics on Network Science

``Don’t think! Feeeeeel’’ is a famous quote by Bruce Lee in the movie Enter the Dragon, and this is my guiding philosophy of learning.

This course is designed to help you feel the concepts and tools of network science through pen-and-paper exercises and hands-on coding.

List of Exercises

Module	Pen and Paper Exercise	Jupyter Notebook
M01: Euler Tour	The Koningsberg Bridge	Jupyter Notebook
M02: Small World	It’s a small world!! 6 degrees of separation	Jupyter Notebook
M03: Robustness	Build it, Break it, and Build it back!	Jupyter Notebook
M04: Friendship Paradox	Data Visualization	Jupyter Notebook
M05: Clustering	Perfect vs. Almost Perfect	Jupyter Notebook
M06: Centrality	Who’s the Big Cheese in the University Clubs?	Jupyter Notebook
M07: Random Walks	Random Walks on Networks	Jupyter Notebook
M08: Embedding	Network Embeddings	Jupyter Notebook
M09: Graph Neural Networks	Graph Neural Networks	Jupyter Notebook

List of assignments

Note: For the students taking SSIE 641, the grading will be done through GitHub Classroom, and the following assignments are not linked to GitHub Classroom. Please use the links provided by the instructor. All the links are listed in Brightspace.

Module	Assignment
Small World	Assignment 1
Network Centrality	Assignment 2
Small world networks	Assignment 3

Intro

• Why should we care networks?
• What is a network?
• Zoo of networks
• Trouble shooting

M01: Euler Tour

• Module 1: Euler Tour
• A puzzle
• Euler’s solution
• Compute with networks
• Exercise

M02: Small World

• Module 2: Small-world
• Small-world experiment
• Wikirace
• Why is our social network small world?
• Walks, Trails, Paths, and Connectedness
• Toolbox for network analysis
• Efficient representation for large sparse networks
• Computing the Shortest Paths and Connected Components
• Assignment
• Appendix

M03: Robustness

• Module 3: Robustness
• Building a cost-effective power grid network
• Minimum spanning tree
• Network Robustness
• Hands-on: Robustness (Random attack)
• Percolation
• Appendix

M04: Friendship Paradox

• Module 4: Friendship Paradox
• In-class experiment
• Friendship Paradox
• Vaccination Game
• Degree distribution

M05: Clustering

• Module 5: Clustering
• What is community?
• Pen and Paper
• Community detection (pattern matching)
• Graph cut
• Balanced cut
• Modularity
• Modularity (Cont.)
• Stochastic Block Model
• Hands-on: Clustering

M06: Centrality

• Module 6: Centrality
• Pen and paper exercises
• What is centrality?
• Centralities based on centralities
• Computing centrality with Python
• Assignment

M07: Random Walks

• Module 7: Random Walks
• Ladder Lottery
• Random Walks
• Pen and paper exercises
• Random Walks in Python
• Characteristics of Random Walks
• Random walks unify centrality and communities

M08: Embedding

• Module 8: Embedding
• Pen and paper exercises
• Spectral Embedding
• word2vec
• Graph embedding with word2vec
• Spectral vs Neural Embedding
• Software for Network Embedding
• Appendix

M09: Graph Neural Networks

• Module 9: Graph Neural Networks
• Pen and paper exercises
• Preliminaries: Image Processing
• From Image to Graph
• Graph Convolutional Networks
• Popular Graph Neural Networks
• Appendix