Working knowledge of linear algebra and statistics is preferable. Given the diverse academic backgrounds, interests, and technical knowledge of the students, no specific technical background is required, but students are strongly expected to be self-motivated in understanding how the mathematic and quantitative operationalizations capture the social scientific insights behind them and in diving into learning network analytic software/libraries.
Throughout this course, students will work toward a final team project that (a) applies network concepts and measures to study an empirically puzzling social phenomenon with network data or (b) develop new network metrics or more efficient algorithms for existing measures based on social scientific insights of human behavior. The following class activities throughout the course are intended to support and augment the final group project.
- Lectures. Lectures, delivered by the instructors, will cover the concepts, network analysis methods, and their application. Part of the lecture will also be used in relation to the assignments, for Q&A, discussions of the assigned readings, etc.
- Homework assignments. Students will write brief weekly memos to summarize the main ideas of the readings. In addition, there will be short weekly data and analytics assignments designed to build familiarity with the software and analysis techniques discussed in the lectures.
- Midterm exam (open book).
- Final team project. In teams, students will apply all the learning throughout the semester as part of a research project studying an interesting problem or a technological artifact in a field / domain that the team members collectively choose. By the first half of the semester, teams will submit short research proposals outlining the study domain, research question / problem, data and analysis plan, and expected results. Finally, teams will submit a written report and present their results in class in the last week of the course. There will be no final exam.
Evaluation will be based on the following distribution:
- 10% class participation
- 20% homework assignments & project proposal
- 30% midterm exam
- 40% final project
This course does not have a fixed letter grade policy; i.e., the final letter grades will not be A=90-100%, B=80-90%, etc.
The default homework is to thoroughly read the required material before coming to class. In addition, homework assignments consist of two types – (a) written reaction notes for the readings and (b) hands-on data analysis exercises. Students will submit a reaction note for at least two of the readings for each class. For textbook-like reading materials, students will summarize how a method or measure works and what concept or intuition it captures. For research articles, students will summarize the main thesis (one or two sentences), hypotheses, methods, and results. The data analysis exercise homework will be on the network measures and methods covered on a given week. These exercise assignments will mostly occur in the first half of the semester. A handout with instructions for analysis exercises using network analysis software will be posted on the course Canvas "Assignment" space.
Teamwork is an important part of this course. Students are expected to form groups of up to three students and work together on multiple assignments and on the final project. Some assignments have a component that is graded for the entire group and a component that is graded individually. By default, group members will receive identical grades on group assignments. However, the instructor reserves the right to institute peer grading in problematic situations.
The first half of the semester will heavily utilize the following textbook in addition to other research papers and book chapters:
- Wasserman, Stanley and Katherine Faust. 1994. Social Network Analysis: Methods and Applications. Cambridge University Press (A.K.A "W&F").
- Barabási, Albert-László. 2016. Network Science. Cambridge University Press
Bring your laptop to class with necessary network software installed.
Network analytic software programs have different strengths (e.g., large network data vs. extensive network metrics vs. statistical analysis vs. visualization), suitable for different use cases. Therefore, this course is software-agnostic as long as your program can get the job done. While there are a number of considerations (e.g., operating system, network size, metric implementation, programmability) for choosing network analysis software, students are encouraged to use ORA-LITE (Windows) for its accessibility and versatility. Students may also consider the Network library in R, the Networkx library in Python, or iGraph (interfacing R and Python), all of which implement a broad range of data manipulation tools, graph generation algorithms, and metrics. SNAP may be a viable solution for students who plan to analyze large-scale networks in the order of hundreds of millions of nodes. Aside from data analysis, students will use network visualization software, such as Gephi, Cytoscape, and Pajek as well as the visualization functionality in ORA-LITE.
This is a 12-unit course. It is intended that you spend close to 12 hours a week on the course, on average. In general, 3 hours/week will be spent in lecture and 9 hours on reading assignments, homework, and final project preparation.
Late submissions will receive partial credit. Exceptions will be made in extraordinary circumstances, typically involving a family or medical emergency (ideally, your academic advisor or the Dean of Student Affairs should request such exceptions on your behalf). Accommodations for travel (e.g., for interviews) can be made so long as you request it in advance. Always communicate with your team about such issues.
The University Policy on Academic Integrity applies. Expectations regarding academic honesty and collaboration for both group work are the same as for individual work, elevated to the level of "group." Group members will collaborate with one another, but groups should work independently from one another. Within groups, we expect that you are honest about your contribution to the group's work. This implies not taking credit for others' work and not covering for team members that have not contributed to the team.
The course also includes individual assignments and individual components of group assignments. Although your solutions for individual parts may be based on the content produced for the group component, you are expected to complete individual components independently of your group members.
It is our intent that students from all diverse backgrounds and perspectives be well served by this course, that students’ learning needs be addressed both in and out of class, and that the diversity that students bring to this class be viewed as a resource, strength and benefit. It is our intent to present materials and activities that are respectful of diversity: gender, sexuality, disability, age, socioeconomic status, ethnicity, race, and culture. Your suggestions are encouraged and appreciated. Please let us know ways to improve the effectiveness of the course for you personally or for other students or student groups.
If you wish to request an accommodation due to a documented disability, please inform the instructor as soon as possible and contact Disability Resources at 412.268.2013 or access@andrew.cmu.edu.
Most homework assignments have a component that require discussing issues in written form. For assistance with written or oral communication, visit the Global Communication Center (GCC). GCC tutors can provide instruction on a range of communication topics and can help you improve your papers, presentations, and job application documents. The GCC is a free service, open to all students, and located in Hunt Library. You can make tutoring appointments directly on the GCC website: http://www.cmu.edu/gcc. You may also browse the GCC website to find out about communication workshops offered throughout the academic year.
The last few years have been challenging. We may all still be under a lot of stress and uncertainty. We encourage you to find ways to move regularly, eat well, and reach out to your support system or the instructors if you need to.
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.