Course Objective
This course will cover current trends in representing, modeling, processing and analyzing social information. Particular emphasis will be on graph based techniques for social network analysis, and on the utilization of parallel and distributed algorithm techniques and programming tools (such as MPI) for large scale processing. Topics in the course will include challenging problems in the field such as community detection, influence spread and centrality analysis. Application domains covered in the course may include social media, epidemiology, and political science models. Programming experience in C++ or Java is expected. Prior experience with parallel and distributed computing is helpful.
Please refer
blackboard for lecture notes, assignments and project details.
Prerequisites
A course in algorithms such as CS 430 or related course.
Recommended textbook
- Stanley Wasserman and Katherine Faust, “Social Network Analysis: Methods and Applications”, 1st Edition, Cambridge University Press, 1994.
For reference:
- Ananth Grama, George Karypis, Vipin Kumar, and Anshul Gupta, “Introduction to Parallel Computing”, 2nd Edition, Pearson, 2003.
Lecture slides, relevant research papers,reading assignment and assignments will be posted on the course website.
Course topics and tentative schedule
| Week | Topics |
| Week 1 | Introduction to Social Computing, Social Network Analysis |
| Week 2 | Graph theory basics, SNA metrics, Centrality Analysis |
| Week 3 | Centrality Analysis, Community Detection |
| Week 4 | Introduction to Parallel computing |
| Week 5 | Parallel algorithm design for SNA |
| Week 6 | Parallel algorithm design for SNA |
| Week 7 | Parallel programming (MPI) |
| Week 8 | Parallel programming (MPI) |
| Week 9 | Parallel algorithm design for SNA |
| Week 10 | Small world networks, Link analysis |
| Week 11 | Other modeling techniques in Social Computing such as Agent based Modeling |
| Week 12 | Modeling in Application domains (epidemiology) |
| Week 13 | Modeling in Application domains (political science) |
| Week 14 | Emerging research problems and wrap up |
Grading
| Assessment | Comments | % |
| Homework Assignments | Around 3-5 | 20% |
| Midterm Exam and Final Exams | | 20% |
| Research Topic Presentation | Group(Around 6 groups) | 30% |
| Class Project | Individual | 20% |
| Class Participation | | 10% |
Course Outcomes
- Learn problem-solving and analytical skills that are needed for doing research in this field.
- Understand fundamental challenges, state of the art methodologies and current trends in socialcomputing.
- Employ computational tools to model and analyze with large and dynamic data and learn to interpret the results.
- Survey a research topic in the field and lead the class in discussions.
Honor Code
The university academic dishonesty policies are in force for the course. Please refer to the handbook for details. Students will not collaborate on assignments or homeworks unless it is explicitly allowed. Students will also read the College of Science academic integrity pledge.
