CASOS Course Information
Choose the course below to get more information about class offerings and course description.
17801/17685/19640 Dynamic Network Analysis
Dynamic Network Analysis - PhD level # 17-801, Masters Level # 17-685, EPP # 19-640
| Instructor: | Dr. Kathleen M. Carley |
| Units: | 12.0 |
Offered Spring 2024
Mondays and Wednesdays 4:00pm - 5:50pm
Recitation Fridays from 4:00pm - 5:50pm
Room Number: Virtually on Zoom and in Tepper 1403
Course Description:
Who knows who? Who knows what? Who is influential? What is the social network, the knowledge network, the activity network? How do ideas, products & diseases propagate through groups and impact these networks? Does social media change the way these networks operate? Questions such as these & millions of others require a network perspective and an understanding of how ties among people, ideas, things, & locations connect, constrain & enable activity. In the past decade there has been an explosion of interest in network science moving from the work on social networks and graph theory to statistical and computer simulation models. Network analytics, like statistics, now plays a role in most empirical fields, and is a fundamental leg of data science.
Network science is a broad and multi-disciplinary field. In this class, students will: gain an appreciation of the history of the field; gain experience analyzing social, semantic, and trail based networks, gain an understanding of the difference between social networks, social media and artificial intelligence; the difference graph-based metrics for network analysis and graphical models; gain experience with the use of traditional and high dimensional network models, and the advances in this field. Applications and issues discussed will include: social media analytics, semantic networks, task networks, organizational design and teams, machine learning and network analysis, generative models, terrorism and crime, health, and fake news. Methods for network data collection, analysis, visualization, and interpretation are covered. Students produce original research in which network data is analyzed using the methods covered in the class.
17621/17821 Computational Modeling of Complex Socio-Technical Systems
Computational Modeling of Complex Socio-Technical Systems - # 17-621/17-821
| Instructor: | Dr. Kathleen M. Carley |
| Units: | 12.0 |
*Fall semesters even number years
Course Description
How likely is an intervention like social distancing to save lives? Will a law legislating sanctions against social media platforms that spread disinformation stop the spread? We live and work in complex adaptive and evolving socio-technical systems where questions such as these arise constantly. Questions such as these are often only addressable through computational modeling, i.e., through simulation. Simulation models are a critical method for understanding how to adaptation and learning will change the status-quo. Computational modeling can be used to help analyze, reason about, predict the behavior of, and possibly control complex human systems of "networked" agents. Using simulation it is possible to advance theory, test policies before enacting them, and think through non-linear social effects.
This course is about computer-based simulation. Students learn how to design, analyze, and evaluate computational models. Students will gain experience with: 1) both agent-based modeling and system dynamics modeling; 2) designing and running virtual experiments with a simulation model; 3) validating simulation models. Additional topics covered include, relation of AI and simulation, cognitive simulation models, modeling frameworks, model docking, and hybrid models.
Take care of yourself. Do your best to maintain a healthy lifestyle this semester by eating well, exercising, avoiding drugs and alcohol, getting enough sleep and taking some time to relax. This will help you achieve your goals and cope with stress.
All of us benefit from support during times of struggle. You are not alone. There are many helpful resources available on campus and an important part of the college experience is learning how to ask for help. Asking for support sooner rather than later is often helpful.
If you or anyone you know experiences any academic stress, difficult life events, or feelings like anxiety or depression, we strongly encourage you to seek support. Counseling and Psychological Services (CaPS) is here to help: call 412-268-2922 and visit their website at http://www.cmu.edu/counseling. Consider reaching out to a friend, faculty or family member you trust for help getting connected to the support that can help.
If you or someone you know is feeling suicidal or in danger of self-harm, call someone immediately, day or night:
CaPS: 412-268-2922
Re:solve Crisis Network: 888-796-8226
If the situation is life threatening, call the police:
On campus: CMU Police: 412-268-2323
Off campus: 911