Calendar

Note

Schedule below is approximate and subject to change

Week 1

Jan 9th (Monday)

• Introductions and welcome

• Set up development environments

• Work on the mini-assignment

• What are networks (and why should we care)?

• Course roadmap (go over this calendar)

• Representing networks

• Look at datasets (if time)

Jan 10th (Tuesday)

Due during class

Have a working Python environment to use for this course (during class).

Due end of day

Complete the mini-assignment.

• Plotting networks

• Connected components

• Centrality measures

  • Degree, strength

  • Eigenvector centrality

  • PageRank

  • Betweenness centrality

• Look at datasets (if time)

Jan 11th (Wednesday)

Due during class

Know what dataset you’ll be working on for the final project.

Complete exit survey to submit this info.

• Random graph models

  • ER

  • DCER

  • SBM

  • DCSBM

  • RDPG

  • Barabasi-Albert

  • Extensions

Jan 12th (Thursday)

Due during class

Be able to load in and somehow plot your dataset(s) in Python.

• Community detection

  • Modularity

    • Simple node-moving

    • Spectral

    • Louvain, Leiden

    • Issues with modularity maximization

      • Resolution limit

      • Overfitting

  • Brief tour of other approaches

  • Application to finding communities in ___

Jan 13th (Friday)

• Network embedding (Part 1)

  • Word2Vec to DeepWalk to Node2Vec

  • Recommendations using an embedding

Week 2

Jan 16th (Monday)

Warning

NO CLASS - Martin Luther King, Jr. Day

Jan 17th (Tuesday)

Warning

Class taught remotely; see videos on course Canvas

• Graph matching

  • When could we use it

  • Why is it hard?

  • Fast approximate quadratic algorithm

  • Code in graspologic

    • Basic

    • Adding seeds

    • Graphs of different sizes

  • Application example

• Ranking

  • Simple rankings (A, A squared)

  • Eigenvector ranking

  • Minimum violations ranking

  • Applications of ranking to study hierarchies

Jan 18th (Wednesday)

• Network embeddings (Part 2)

  • Spectral methods

    • Adjacency spectral embedding

    • Laplacian spectral embedding

    • Two truths

Jan 19th (Thursday)

Due by beginning of class

Submit final project notebooks.

• Multiple network embedding

  • ASE x 2

  • Omnibus embedding

  • Multiple ASE

Friday

Due in class

Final project presentations

Due by end of day

Complete exit survey.

• Final project presentations