CS 7280
Network Science: Methods and Applications

Fall 2020


Teaching mode during Fall'20

During the last few months the instructor has developed an online version of this course. The course consists of 14 Lectures (see below for the list of topics).

Each week, we will cover one of these lectures. Students will first watch (online) the video of the lecture on their own -- and they will read the assigned chapters or research papers. This will count as attending Tuesday's class.

Then, on Thursday at 12:30-13:45pm EST of each week, we will have a BlueJeans session to go through exercises, additional derivations, and to answer your questions in a live, interactive manner.

The course will also include two in-person interactions between the instructor and each student. These interactions will focus on the course project. The first such meeting will take place sometime in late September or October, to discuss your proposed project. The second meeting will be sometime in November to discuss what you have accomplished and to review your final project presentation.

When it is not possible to have these interactions in-person (because of illness or other COVID-related reasons) we will have them online.

Teaching assistant

Student Support Services

Lots of valuable information and links here. Please do not hesitate to ask for help if you ever feel helpless.

Course objectives

It is often the case that complex systems, both living and man-made, can be represented as static or dynamic networks of many interacting components. These components are typically much simpler in terms of behavior or function than the overall system, implying that the additional complexity of the latter is an emergent network property.

Network science is a relatively new discipline that investigates the topology and dynamics of such complex networks, aiming to better understand the behavior, function and properties of the underlying systems.

The applications of network science cover physical, informational, biological, cognitive, and social systems. In this course, we will study algorithmic, computational, and statistical methods of network science, as well as applications in communications, biology, ecology, brain science, sociology and economics. The course will go beyond the strictly structural concepts of small-world and scale-free networks, focusing on dynamic network processes such as epidemics, synchronization, or adaptive network formation.

Course prerequisites

The course hopes to attract students from different academic backgrounds and research interests (including math, physics, engineering, biology, neuroscience or sociology). Consequently, the instructor will try to keep the course as ``self-contained'' as possible. However, some knowledge (at the level of a good undergrad course) with calculus, probability, linear algebra, and programming is necessary. Additionally, students will be free to choose course projects that are closer to their background.

Other constraints

You cannot take this course for "Audit". Doing the homeworks and project is essential.


Together with several research papers, we will cover specific chapters from the following three textbooks:

The following books will be useful references in certain parts of the course:

Syllabus and course structure

Student projects and milestones

Course projects can be of different types: One option is that you select a research paper that you are very interested in and try to reproduce certain resulst in that paper -- using either the same dataset or with a different dataset. Another option is that you attempt to study an original research question. In this case you will need to be careful not to propose something that would normally take much longer than 4-5 weeks. A third option is to implement one or more network analysis algorithms proposed in the literature and evaluate them in a new context. What kind of project would not be acceptable? Anything that does not really relate to this course. Anything that is too similar to other projects done by the student in his/her research or other courses. Anything that seems too easy or too hard (to the instructor at least).

Groups of two students are ideal but individual projects are also acceptable. Larger teams will need instructor approval.

Project milestones

  1. September 18: project proposal
  2. October 16: progress report
  3. November 20: final paper due
  4. TBA: project presentations


Network datasets

There are dozens of sites that provide pointers to network datasets. The following is just a small subset:

Network analysis and visualization tools

The following pointers provide network analysis tools that you can use in course projects: