Data Mining for Social Networks (KSE625)

Fall 2012

  1. Instructor:
    Jae-Gil Lee (Office: E2 2203, Phone: x 1617, E-mail:
  2. Time and Place:
    10:30 a.m. ~ 12:00 p.m. Tuesday and Thursday, E2 1228
  3. Facebook Group: 2012 KSE625 
  4. Course Summary:
    The advent of online social networks has been one of the most exciting events in this decade. Many popular online social networks such as Twitter, Facebook, and LinkedIn have become increasingly popular. Such social networks typically contain a tremendous amount of content and linkage data which can be leveraged for analysis. This abundant data provides unprecedented opportunities for knowledge discovery in the context of social networks. This course teaches key concepts and algorithms for analyzing online social networks from the data mining point of view. The course will cover many interesting topics including community discovery, evolution analysis, link prediction, and influence analysis. The instructor will introduce the representative papers (two for each week) published in the data mining field. In addition, the students will get to play with real data crawled from social networking sites.
  5. Textbooks:
    • Auxiliary textbook: Matthew A. Russell, Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites, O'Reilly, 2011.
    • Auxiliary textbook: Charu C. Aggarwal, Social Network Data Analytics, Springer, 2011.
  6. Grading Policy:
    • Research project (term paper): 30%
    • Midterm exam: 30%
    • Assignments: 20% (latency penalty: 20%)
    • Presentation: 10% (proposal and final report presentations)
    • Quizzes: 10% (once or twice between midterm and presentation)
    • Class participation: optional (deduct 1 point for each absence after 3 absences)
  7. Schedules (subject to change):
  8. Video Lectures:
    The students who enrolled in this course can watch the video lectures recorded last year. Please log in the KLMS using your portal id and password.
  9. Assignments:
  10. Term Paper Policy: policy  teams
  11. Syllabus: download
  12. Teaching Assistants:
    • Seulki Lee (E-mail:
    • Sukhwan Jung (E-mail: