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Welcome to SSIE 641 Advanced Topics on Network Science

Intro

Why should we care networks?
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

Repository
Open issue

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Pen and paper exercises

Pen and paper exercises#

✍️ Pen and paper exercises

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Module 8: Embedding

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Spectral Embedding

By Sadamori Kojaku