Network Analysis & Modeling

CSCI 5352 - Network Analysis & Modeling

Fall 2020

  • Time: Monday, Wednesday, Friday, 1:50pm - 2:40pm
  • Place: email for Zoom link or see Canvas; ECCS 1B28
  • Lecturer: Dan Larremore
  • Office: –Online–
  • Office hours: –TBD–
  • Email: daniel.larremore
  • Teaching Assistant: None
  • Syllabus: PDF


Network science is a thriving and increasingly important cross-disciplinary domain that focuses on the representation, analysis, and modeling of complex social, biological and technological systems as networks or graphs. Modern data sets often include some kind of network. Nodes can have locations, directions, memory, demographic characteristics, content, and preferences. Edges can have lengths, directions, capacities, costs, durations, and types. And, these variables and the network structure itself can vary, with edges and nodes appearing, disappearing and changing their characteristics over time. Capturing, modeling and understanding networks and rich data requires understanding both the mathematics of networks and the computational tools for identifying and explaining the patterns they contain.

This graduate-level course will examine modern techniques for analyzing and modeling the structure and dynamics of complex networks. The focus will be on statistical algorithms and methods, and both lectures and assignments will emphasize model interpretability and understanding the processes that generate real data. Applications will be drawn from computational biology and computational social science. No biological or social science training is required. (Note: this is not a scientific computing course, but there will be plenty of computing for science.)

For all grading and other information, please see the Syllabus PDF.


Problem Sets

Supplemental Readings

Week 1:

Week 2:

Week 3:

Week 4:

Week 5:

Week 6:

Week 7:

Week 8:
See links in Lecture 8 notes.

Week 9:

Week 9 Bonus:

Week 10:

Week 11:

Week 12:

Week 12:

Week 13:

Network Tools
NetworkX, network analysis package (Python)</br> igraph, network analysis tools (Python, C++, R)
graph-tool, network analysis and visualization software (Python, C++)</br> GraphLab, scalable network analysis (Python, C++)</br>

Network Visualization
Cytoscape, network visualization software
yEd Graph Editor, network visualization software
Graphviz, network visualization software</br> Gephi, network visualization software</br> graph-tool, network analysis and visualization software</br> webweb, network visualization tool joining Matlab and d3
MuxViz, multilayer analysis and visualization platform

Network Data Sets
The Colorado Index of Complex Networks (ICON; more than 4000 graphs)
US Census Education-Employment network (social, bipartite, weighted)

Other Courses on Networks
Network Theory (University of Michigan)
Statistical Network Analysis (Purdue University)
Networks (Cornell University)
Networks (Harvard University)
Social and Economic Networks: Models and Analysis (Coursera / Stanford)
Social Network Analysis (Coursera / University of Michigan)
Social and Information Network Analysis (Stanford)
Graphs and Networks (Yale)
Spectral Graph Theory (Yale)
The Structure of Social Data (Stanford)</br>

LaTeX (general) and TeXShop (Mac)
Matlab license for CU staff (includes student employees)
Mathematica license for CU students
NumPy/SciPy libraries for Python (similar to Matlab)
GNU Octave (similar to Matlab)
Wolfram Alpha (Web interface for simple integration and differentiation)
Introduction to the Modeling and Analysis of Complex Systems, by Hiroki Sayama (free online textbook)

Things Worth Reading
Everything you wanted to know about Data Analysis and Fitting but were afraid to ask, by Peter Young
Machine Learning, Statistical Inference and Induction Notebook (by Cosma Shalizi)
Power Law distributions, etc. Notebook (by Cosma Shalizi)
Statistics Done Wrong, The woefully complete guide (by Alex Reinhart)
Some Advice on Process for [Research Projects]