Lecturer Fall 2017: Aaron Clauset
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 scientic computing course, but there will be plenty of computing for science.)
Prerequisites (recommended): CSCI 3104 (undergraduate algorithms) and APPM 3570 (applied probability), or equivalent preparation.
An adequate mathematical and programming background is mandatory. The concepts and techniques covered in this course depend heavily on basic statistics (distributions, Monte Carlo techniques), scientic programming, and calculus (integration and differentiation). Students without sufficient preparation will struggle to keep up with the lectures and assignments. Students without proper preparation may audit the course.
(1) Networks: An Introduction by M.E.J. Newman
(2) Pattern Recognition and Machine Learning by C.M. Bishop
- Mostly lecture-style class, with some guest lectures and some class discussions.
- Problem sets (6 total, worth 50% of grade) due every 2 weeks throughout the semester.
- Class project (worth 30% of grade) due at end of semester.
- No exams.
- Networks are cool.
Piazza Class Discussion: We will use Piazza for class discussion and Q&A. The system is designed to help you get help from classmates and myself. Rather than emailing questions to the teaching staff, please post your questions on our Piazza forum.
- Week 1 Introduction and overview
- Week 2 Measures of structural importance
- Week 3 Random graphs I: homogeneous degrees
- Week 4 Random graphs II: heterogeneous degrees
- Week 5 Large-scale structure I: modularity, assortativity, homophily
- Week 6 Large-scale structure II: stochastic block models
- Week 7 Spreading processes on networks
- Week 8 Large-scale structure III: more block models
- Week 9 Wrangling network data I: sampling
- Week 10 Wrangling network data II: auxiliary information
- Week 11 Spatial networks
- Week 12 Growing networks
- Week 13 Fall break
- Week 14 Dynamic networks
- Weeks 15-16 Project presentations
Grades will be assigned based on (on-campus) a 20% attendance, 45% problem sets, and 35% project division, or (off-campus) a 60% problem sets, and 40% project division.
Letter grades will not be assigned until after all work for the semester has been submitted and graded. In the meantime, only numerical grades will be tracked.