DSC 480 Social Network Analysis
Summary
Social Network Analysis (SNA) is an exciting field that studies the impact of social networks on many areas including business, health, and the social sciences. Starting with the relationship between two individuals vast networks are examined looking for individuals that utilize these networks as communication, power, and location. Uses include preventing terrorism, viral messaging, marketing, people analytics, virus contagion, communication, and diffusion.
In data science inference is generally used where individuals are understood through their attributes i.e. gender, age, education, salary, region, religion, ethnicity, including height and weight. In supervised learning techniques like regression are used to show relationships between individual attributes and variables. Logistic regression is one classic examples where how much salary is needed before an individual purchases a car can be determined. Unsupervised learning techniques cluster these individuals by the characteristics in their choices. Individuals will choose cars based on preferences and situations like whether they have a family, are in the midst of a mid-life crisis, are concerned about their safety, looking for cheaper travel, or displaying their status.
In social network analysis it is the characteristics of the network not the individual. The network may be impacted by attributes but is not determined by them. The same patterns of relationships exist no matter the gender, age, and other attributes even when the networks are separate. The network can be described by size, density, and similar network attributes as well as inferences as to how the network is growing. One interesting example was an exercise where an unknown network was given in hour increments over a 24-hour period to a group of analysts. They were able to predict murders, alliances, factions, and changes in power. They [the analysts] thought this was related to current events in the Middle East. It turned out to be Shakespeare’s play Julius Caesar.
In this class we will provide a deep introduction to social network analysis. This will occur using tools like R, statistics, and social data.
Course Objectives
- Think in terms of social networks
- Have a developed understanding of the field of Social Network Analysis
- Use the appropriate statistics to measure social networks
- Follow a standardized method to gather-process-interpret network data
- Input data files in multiple formats using R
- Process data using social network packages
- Interpret output using R and tools including R-Markdown
- Use Exponential Random Graph Models i.e. equivalent to regression
Texts
Required:
[Borgatti] Borgatti, Stephen, Everett, Martin, Johnson, Jeff, Agneessens, Filip (2022) Analyzing Social Networks Using R. Sage Publications. ISBN 978-1529722476 Readings are from the book as well as the R library needed for the course.
Grading
Grading is based:
50% Assessments on 2 projects for midterm and final
40% Assignments
10% Participation
In general work that is above expectations gets an A, work that meets expectations gets an A-, and work that has deficiencies gets B+ or below depending upon the level of deficiencies. If work is not turned in then this is graded to F. Work is expected to be on time, however if there is something preventing this contact me prior to the due date.
Prerequisites
An understanding of linear regression at the level of DSC 423, SOC 412, or PSY 411. Parts of the course will build upon these concepts. A basic familiarity with R is also needed. There are many resources for R that are free on the web and a book is recommended for the class. This is not a programming class, however loading files, putting them into data frames, adding libraries, running commands both interactive and through scripts will be needed. Other R commands that come from the Tidyverse will be introduced in class based on need.
This syllabus is subject to change as necessary during the quarter. If a change occurs, it will be thoroughly addressed during class, posted under Announcements in D2L and sent via email.
Evaluations are a way for students to provide valuable feedback regarding their instructor and the course. Detailed feedback will enable the instructor to continuously tailor teaching methods and course
content to meet the learning goals of the course and the academic needs of the students. They are a requirement of the course and are key to continue to provide you with the highest quality of teaching. The
evaluations are anonymous; the instructor and administration do not track who entered what responses. A program is used to check if the student completed the evaluations, but the evaluation is completely
separate from the student’s identity. Since 100% participation is our goal, students are sent periodic reminders over three weeks. Students do not receive reminders once they complete the evaluation.
Students complete the evaluation online in CampusConnect.
This course will be subject to the university's academic integrity policy. More information can be found at http://academicintegrity.depaul.edu/ If you
have any questions be sure to consult with your professor.
All students are expected to abide by the University's Academic Integrity Policy which prohibits cheating and other misconduct in student coursework. Publicly sharing or posting online any prior or current materials from this course (including exam questions or answers), is considered to be providing unauthorized assistance prohibited by the policy. Both students who share/post and students who access or use such materials are considered to be cheating under the Policy and will be subject to sanctions for violations of Academic Integrity.
All students are required to manage their class schedules each term in accordance with the deadlines for enrolling and withdrawing as indicated in the University Academic Calendar. Information on enrollment, withdrawal, grading and incompletes can be found at http://www.cdm.depaul.edu/Current%20Students/Pages/PoliciesandProcedures.aspx.
Students who feel they may need an accommodation based on the impact of a disability should contact the instructor privately to discuss their specific needs. All discussions will remain confidential.
To ensure that you receive the most appropriate accommodation based on your needs, contact the instructor as early as possible in the quarter (preferably within the first week of class), and make sure that
you have contacted the Center for Students with Disabilities (CSD) at:
Lewis Center 1420, 25 East Jackson Blvd.
Phone number: (312)362-8002
Fax: (312)362-6544
TTY: (773)325.7296