CMSC601-0101,PJ01/PHYS798Z-0101,PJ01: Computational and Mathematical Analysis of Biological Networks across Scales-Fall 2026 mpop

Essential details

Class location: IRB 2107
Class time: MW 2-3:15 PM

Overview

This course is an introduction to computational and mathematical analysis of complex networks, as they occur in various applications, with a particular emphasis on biological networks. Example application areas include gene networks, real and artificial neural networks, and ecological networks. Topics include: network measures, mechanistic network models, network algorithms and inference, network dynamics, and network visualization. The course is aimed for a broad interdisciplinary graduate student audience and we anticipate enrollment from the physical & mathematical sciences, computer science & engineering, and life sciences.

Prerequisites

A strong undergraduate math background that includes some calculus, differential equations, linear algebra, and basic probability and statistics is assumed. Some experience with computer programming is necessary. Problem sets will include numerical calculations and simple simulations, which means either writing code in a scientific programming environment like Python, R, or Matlab. For most of the problem sets, you’ll be able to choose your programming language, but some may require the use of a particular environment. In these cases, links to relevant tutorials will be included.

Textbooks

The course will primarily rely on two free textbooks:

Network Science by Albert-László Barabási - https://networksciencebook.com

The Atlas for the Aspiring Network Scientist by Michele Coscia - https://www.networkatlas.eu 

 

Students who are interested in digging deeper into network science my find the following books also useful:

Networks, 2nd Edition by Mark Newman (highly recommended) - https://academic.oup.com/book/27884 

A First Course in Network Science by Menczer, Fortunato & Davis (strongly recommended for those with limited programming background) - https://www.cambridge.org/highereducation/books/a-first-course-in-network-science/EE22722F27519D8BB1443C7225C57BAF . (Code associated with the book is available here: https://cambridgeuniversitypress.github.io/FirstCourseNetworkScience/ ).

Logistics

Instructor: Mihai Pop (he/him)
Office: IRB 3222
Office hours: TBD

Important Dates

August 31, 2026 - First day of class
September 7, 2026 - Labor day  (no class)
October 12, 2026 - Fall break (no class)
November 25, 2026 - Thanksgiving break (no class)
December 9, 2026 - Last day of class.

Learning Outcomes

At the end of this course, students will be able to:

• Describe common  uses of network science in the analysis of biological data
• Describe several approaches for computationally and mathematically modeling networks
• Describe approaches for visualizing network data and determine the appropriate data visualization for the data science questions being asked
• Describe key concepts related to networks and network science
• Describe key algorithms for analyzing network structure and assess their relative merits and appropriateness for different applications
• Implement code for analyzing network data
• Use network science to analyze real biological data sets

Topics covered

1. Introduction to graphs/networks and different types
2. Introduction to computer tools for processing networks
3. Node-based measures of graphs (and what they can say, how they are used). Scale-free property.
4. Path-based measures of graphs (and what they can say, how they are used).
5. Generative models of graphs (and why they matter, how they match the truth) 
6. Community detection in networks.
7. Diffusion/transmission models and dynamic networks.
8. Machine learning on networks.
9. Drawing/visualizing networks

For students who are parents

I welcome and support students who are parents. I appreciate that parenthood presents unique challenges and demands on a student’s time and availability. If childcare constraints present an issue with class attendance, I invite student parents to work with me to discuss alternate arrangements. I also welcome and support pregnant students and will provide possible accommodations and discuss arrangements so that students can complete course requirements.

Overall principles

• Slides will not be shared - most material discussed in class is found in the two recommended free textbooks. At the end of each lecture we'll post in ELMS an updated list of topics that were discussed in class
• This is a graduate class - it is the students' responsibility to take notes, catch up on the material if they missed class, and to seek additional resources in case they lack background relevant to some of the topics being presented. 

Resources

Help Center (301-314-HELP) is University of Maryland’s student-run peer counseling and crisis intervention hotline that provides free and confidential help to the UMD community.

Code of conduct

I am personally committed to promoting a supportive and inclusive environment within the University, and I hope you will assist me in doing so. Please recognize the broad diversity of the students in the class across many dimensions: gender, sexual orientation, ethnicity, race, socioeconomic status, political views, educational background,  learning style, etc.  Irrespective of the visible and invisible differences between us, everybody is here to learn, and we are all responsible for creating a supportive environment in which each of us can grow. 

Be polite. Allow all an opportunity to speak. Recognize that failure is an important part of learning: take risks answering questions even if uncertain of the answer, and support those who do the same. Help those who struggle. While in class, focus on the class, and not on the many other things going on in your life.

Please also review the University's code of student conduct.

Grading

More details presented on the grading details page. 

Grading will be determined based on the following elements:

• problem sets
• literature presentations
• class projects
• class participation and discussion (including peer review)

We will use a "specification grading" strategy this semester.  All assignments will be graded on a 3-level scale:

1. Needs improvement. The assignment did not meet the expectations.
2. Adequate. The assignment met expectations but had significant room for improvement.
3. Excellent. The assignment met expectations and only minor problems.

The final grade will be assigned according to the following rubric:

grading rubric

A	Adequate or Excellent on all assignments. At least two assignments receive an Excellent rating.
B	Adequate or Excellent on more than 80% of the assignments. The class project is marked Adequate or better.
C	Adequate or Excellent on more than 50% of the assignments. The class project is marked Adequate or better.
F	Receives "Needs Improvement" on more than 50% of the assignments OR receives "Needs Improvement" on the class project.

Late assignment policy: If you anticipate running late for an assignment you must inform me ahead of time and indicate a day/time by when you will be able to complete the assignment (which now becomes the hard deadline, if approved by the instructor). This does not apply to sudden illnesses or other urgent circumstances (which are covered by the accommodation policy). The emphasis here is on "urgent", i.e., situations in which one would not reasonably expect you to be able to inform me before the deadline.  

Beyond these conditions, all assignments submitted late are marked as "Needs improvement". 

Attendance policy

This is an in-person class. Lectures are not recorded and slides are not provided. If you miss class you will have to talk to your colleagues to find out what you missed. Any announcements/clarifications made during lectures supersede information provided in the syllabus or other information provided on ELMS.

Accommodations

Accommodations for religious observances

You must notify me within 2 weeks from the beginning of class if you will miss a class or exam due to a religious observance.  Requests received after the first two weeks of class will be accommodated.

Accommodations for disabilities

You must notify me within 2 weeks from the beginning of class if you require special accommodations due to disabilities.  All such requests must be accompanied by documentation from the Accessibility and Disability Service. Requests received after this deadline may not be accommodated.

IMPORTANT: Accommodations are forward-looking, i.e., they only apply to class assignments that take place after I receive a formal accommodation letter from ADS. It is, thus, important to inform me and relevant campus parties, in a timely manner, of any condition that prevents you from fully participating in the class. Retroactive accommodations will not be granted.

Academic honesty

Simply put: cheating will not be tolerated under any circumstance.  While you are allowed to prepare for assignments together with colleagues (or by using AI), you must complete all individual assignments by yourselves. 

Plagiarism. Your answers to assignments (including code), unless explicitly allowed by the assignment, must be your original contribution. If you must quote information from other sources (including ChatGPT or other AI assistants), please keep the quotes short and attribute the source.

Further information on academic integrity is available here: http://www.cs.umd.edu/class/resources/academicIntegrity.html 

Illness/emergency preparedness

See http://prepare.umd.edu for more information.

Please familiarize yourself with guidelines related to an active shooter event: .

If you are sick, don't come to class - no need to get others sick. If you are recovering from an illness and still exhibit minor symptoms, please wear a tight-fitting mask (KN95 or equivalent).

Please report sickness through ELMS to the instructor.