Social Network Analysis

Fall 2011 (14:20 ~ 17:20, Thur, CSIE102)

With the emergence of Facebook, Twitter, and Plurk, the idea of 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.

Note that in this course we will NOT teach how to program in Facebook or some other social media. We will teach only how to analyze social network datasets.

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: Jing-kae Lo (kaeaura@iis.sinica.edu.tw) , xTv (r99944049@ntu.edu.tw), L޳(wei.3729@gmail.com)

Time: 14:20 ~ 17:20, Thu

Classroom: CSIE R102
Grading:

3~4 Programming Assignments (60%)
Final Project or Final Competition (30%)
Paper Presentation (10%)

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	out	due
9/22	basics & small world, power law, random graph	hw1
9/29	dynamic social network
10/6	process model (diffusion)	hw2	hw1
10/13	process model (attack)
10/20	Paper presentation 1		hw2
10/27	community detection I	hw3
11/3	community detection II, position analysis
11/10	Community Detection III + HW1 Discussion
11/17	link prediction & learning		hw3
11/24	paper presentation 2 (link discovery)	hw4
12/1	Heterogeneous social networks+ hw2 discussion
12/8	Potential topics for project+ Hw3 discussion
12/15	project proposal presentation		hw4
12/22	paper presentation 3
12/29	Microblog Analysis + Sampling for Social Networks + hw4 discussion
1/5	final project presentation 1
1/12	final project presentation 2