Social Network Analysis and Graph Mining

Fall 2009 (14:20 ~ 17:20, Tuesday, CSIE R102)

The terminology "Social Network" has become very popular in recent years. Technically speaking, social network is simply a kind of data structure that encodes the relationships in between objects (e.g. people, organization, places, etc). So, what is the magic about it? Why it becomes one of the sexiest terms in research? We will try to uncover the beauty of it throughout this course.

Social Network Analysis (SNA) is an interdisciplinary study that can be tackled from different aspects including sociology, network science, data mining and machine learning, or even marketing. In this course, we will discuss how one can analyze, model, predict, and explain the behavior of large and complex social networks. It would be CS-oriented while the students are required to design/implement the methodologies and test on the real-world social networks.

Course Goals:

Conveying the basic ideas and advanced technologies in social network analysis
Introducing some graph mining packages/tools
Equipping learners with capability of implementing a social network analysis system
Discussing state-of-the-art research results in social network analysis

Lecturer: Shou-de Lin (office: CSIE R333)

TA: Cheng-Te Li (office: CSIE R302), TA Time: TBA

Course Time: 14:20 ~ 17:20, Tuesday

Classroom: CSIE R102, Website: http://mslab.csie.ntu.edu.tw/snam2009/backup.html

Grading:

Four Programming Assignments (55%)
Paper Presentation (10%)
Final Project (30%)
Course Participation (5%)

General References

S. Wasserman and K. Faust. Social Network Analysis: Methods and Applications. Cambridge University Press, UK, 1994.
P. J. Carrington, J. Scott, and S. Wasserman. Models and Methods in Social Network Analysis. Cambridge University Press. 2005.
Hanneman, Robert A. and Mark Riddle, Introduction to social network methods, (Online Text Book), Riverside, CA: University of California, Riverside, 2005.
M. E. J. Newman. The Structure and Function of Complex Networks. SIAM Review, 45:167-256, 2003.
L. Getoor and C.P. Diehl. Link mining: a survey. ACM SIGKDD Explorations, 7(2), 2005.

Syllabus (tentative)

9/15	Intro, basics
9/22	basics & small world	homework 1.1 out
9/29	power law, random graph	homework 1.2 out
10/6	process model
10/13	paper presentation 1	homework 1 due
10/20	community detection (I)
10/27	community detection (II)	homework 2 out
11/3	social position analysis
11/10	Paper presentation 2
11/17	Outlier detection & centrality	homework 2 due
11/24	Heterogeneous social network Mining (I)	homework 3 out
12/1	Heterogeneous social network Mining (2)
12/8	Project Proposal & Homework Discussion
12/15	Dynamic Social Networks	homework 3 due
12/22	link prediction & learning
12/29	paper presentation 3
1/5	final project presentation 1
1/12	final project presentation 2	Project Report Due