CS579: Online Social Network Analysis

Illinois Institute of Technology

If you've joined the class late, please check Blackboard for announcements, and please complete the course survey ASAP.

Course: CS 579: Online Social Network Analysis
Instructor: Dr. Aron Culotta
Meetings: 1:50 - 3:05 pm M/W Life Sciences Bldg 111
E-mail: culotta at cs.iit.edu
Phone: 312-567-5261
Office Hours: M/W 3:05 p.m. - 4:00 p.m.
Office: Stuart Hall 229B
TAs:

• Junzhe Zheng (jzheng9 at hawk.iit.edu); office hour 2-3 p.m. Tuesdays
• Alaa Ayach (aayach at hawk.iit.edu); office hour 12:45-1:45 p.m. Tuesdays 007 SB

See the Schedule for a detailed list of readings and due dates.

Description: This course will explore the latest algorithms for analyzing online social networks, considering both their structure and content. Fundamentals of social graph theory will be covered, including distance, search, influence, community discovery, diffusion, and graph dynamics. Fundamentals of text analysis will also be covered, with an emphasis on the type of text used in online social networks and common applications. Topics include sentiment classification, information extraction, clustering, and topic modeling. Emphasis will be placed on the application of this technology to areas such as public health, crisis response, politics, and marketing. Prerequisite: CS430

Readings:

• Networks and Markets: Reasoning about a highly connected world, David Easley and Jon Kleinberg (ek)
• Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman, Jeff Ullman (lru)

Grading:

200 points - Assignments (4 @ 50 points each)
100 points - Test 1
100 points - Test 2
200 points - Project
600 total points

Percent	Grade
100-90	A
89-80	B
79-70	C
69-60	D
< 60	E

Academic Integrity

• Please read IIT's Academic Honesty Policy
• All work you turn in must be done by you alone, except for the group project.
• All violations will be reported to academichonesty@iit.edu.
• The first violation will result in a failing grade for that assignment/test. The second will result in a failing grade for the course.

Late Submission Policy

• Late assignments will not be accepted, unless:
  • There is an unavoidable medical, family, or other emergency.
  • You notify me prior to the due date.

Objectives:

1. Provide understanding of the theoretical foundations of graph analysis, including clustering, search, homophily, and diffusion.
2. Provide understanding of the theoretical foundations of text analysis, including classification, clustering, and information extraction in the context of network and text media analysis.
3. Practice design and implementation of a system that applies the principles of graph and text analysis to a problem in online social networks

Contribution to general objectives:

a. An ability to apply knowledge of computing and mathematics appropriate to the discipline.
c. An ability to design, implement and evaluate a computer-based system, process, component, or program to meet desired needs.
f. An ability to communicate effectively with a range of audiences.
i. An ability to use current techniques, skills, and tools necessary for computing practices.
j. An ability to apply mathematical foundations, algorithmic principles, and computer science theory in the modeling and design of computer-based systems in a way that demonstrates comprehension of the tradeoffs involved in design choices.
k. An ability to apply design and development principles in the construction of software systems of varying complexity

Similar Courses:

1. Social Media Analysis 10-802, Carnegie Mellon
2. Social Media Analysis, CSCI 599, ISI
3. Social Networking: Technology and Society, INFM 289I, University of Maryland
4. Networks, CS 2850, Cornell
5. Social and Information Network Analysis, CS224W, Stanford
6. The Structure of Information Networks, CS 6850, Cornell
7. Models of Social Information Processing, SI301, Michigan
8. The Structure and Dynamics of Networked Information, CS673, USC
9. Online Social Networks and Media, CS14, University of Ioannina
10. Information Networks, Stanford
11. Social and Technological Network Analysis, Cambridge