Instructor
Santiago Segarra (segarra@rice.edu)
http://segarra.rice.edu
office: Duncan Hall 2047
office hours: TBD
Time and Location
Thursday 4:00pm-6:50pm, location TBD
Short Description
This course provides an introduction to complex networks, their structure, and function, with examples from engineering, biology, and social sciences. Topics include spectral graph theory, notions of centrality, community detection, random graph models, inference in networks, opinion dynamics, and contagion phenomena. Our main goal is to study network structures and how they can be leveraged to better understand data defined on them.
Prerequisites
Basic linear algebra, basic probability and statistics, and ability to program in Python.
Learning Outcomes
This course will provide students with the mathematical tools and computational training to understand large-scale networks in the current era of Big Data. It is our objective that after taking this course the students will be able to model a variety of systems as networks and will be able to compute and interpret structural descriptors of these models. Moreover, students will be able to make statistical predictions of unobserved portions of the network systems and model the potential evolution of processes on the network. Finally, our objective is that students will be able to use networks as a universal language to communicate across different application domains, providing the opportunity to implement engineering solutions to seemingly unrelated fields.
Grading Policy
Homework (40%) and final project (60%).
Recommended Texts
There is no required text. The main material is contained in the course slides and supported by in-class derivations on the board. However, as additional references and for the curious students that want to expand on these topics, we recommend the following books:
[1] Newman, M. (2010). Networks: an Introduction. Oxford university press.
[2] Kolaczyk, E. (2009). Statistical Analysis of Network Data: Methods and Models. Springer Series in Statistics.
[3] Easley, D. and Kleinberg, J. (2010). Networks, Crowds, and Markets: Reasoning about a Highly Connected World. Cambridge University Press.
Collaboration Policy and the Rice Honor Code
We encourage that you work together whenever possible: problem sets and general discussion of the material. Keep in mind, however, that for the problem sets the solutions you hand in should reflect your own understanding of the class material, and should be written solely by you. It is not acceptable to copy a solution (theory or code) that somebody else has written.
In this course, all students will be held to the standards of the Rice Honor Code, a code that you pledged to honor when you matriculated at this institution. If you are unfamiliar with the details of this code and how it is administered, you should consult the Honor System Handbook at http://honor.rice.edu/honor-system-handbook/. This handbook outlines the University’s expectations for the integrity of your academic work, the procedures for resolving alleged violations of those expectations, and the rights and responsibilities of students and faculty members throughout the process.
Disability Support Services
If you have a documented disability or other condition that may affect academic performance you should: 1) make sure this documentation is on file with Disability Support Services (Allen Center, Room 111 / adarice@rice.edu / x5841) to determine the accommodations you need; and 2) talk with me to discuss your accommodation needs.