T-79.7003 Graphs and Networks

T-79.7003 Graphs and Networks

General Information

Instructor: Charalampos E. Tsourakakis 
Classroom: Lecture hall T5 (CS building)
Original Web page link: T-79.7003
Time: Fri 2-5pm
Email: tsourolampis at gmail dot com.

Grading

  • 3 homeworks, 5% each (total 15%)
  • 2 tests, 30% each (total 60%)
  • 1 Project, 25%

Homework policy

You may discuss the problems with other students but you must write your solutions
on your own and list your collaborators. You may not search the Web for solutions.
You may consult outside materials, but you must cite your sources in your solutions.

Violation of the policy may result in receiving a zero grade for the question or the assignment  or even the whole course. The penalty will be assessed at the instructor’s discretion.

Handouts

  • Midterm exam (Oct. 18, 2013)
  • Fall midterm break (Oct. 25, 2013)
  • Lecture 9 (Nov 22, 2013): One hour guest lecture on boostrap percolation by Dr. Thomas Vallier + project presentations
    • Project Presentations
      • Orestis
      • Hristo
      • Miquel
      • Abdulmelik
  • Lecture 10 (Nov 29, 2013): Project presentations
    • Geraud, Louiza, Sanja
    • Eric, Emanuelle
    • Ehsan
    • Polina
  • Finland’s Independence Day (Dec 6, 2013)

Data Sources

Stanford Large Network Dataset Collection (SNAP) 
The University of Florida Sparse Matrix Collection (UFS) 
Twitter graph (2009) 

Software, Libraries

NetworkX (Python)
JUNG (Java)
Stanford Large Network Analysis (SNAP system) (C++, Jure Leskovec)
MATLAB BGL (Matlab, David Gleich)
Mathematica, Graph and Network Modeling (Mathematica)

Visualization software

GraphViz
Gephi 

Related Web pages

David Aldous (Berkeley) 
Easly-Kleinberg (Cornell) 
Dan Spielman (Yale)

Books, Notes

Rick Durrett’s book 
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Remco Van Der Hofstad’s notes 
Alan Frieze’s notes (handwritten)

Suggested papers

A list of suggested papers follows. I have grouped the papers by category to facilitate your search, but it is worth outlining that some papers could belong to more than one category.

Stochastic graph models

Estimating models from network data

Strategic graph models

Diffusion related (rumor spreading, cascades, network reconstruction etc.)

Social learning

Subgraphs

Dense subgraphs and graph partitioning

Evolutionary dynamics on graphs

Learning

Various

Note

You are not obliged to pick one of the aforementioned papers. You can choose a paper of your own preference as long as it is related to the content of the class. Some conferences and journals where you can search for more papers follow.

  • ACM Conference on Electronic Commerce (EC)
  • KDD
  • WWW
  • WSDM
  • ESA
  • ICALP
  • SODA
  • Random structures and algorithms (RSA)
  • Combinatorics, Probability and Computing (CPC)
  • Internet Mathematics

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