Social Network Analysis

Fall 2014 (14:20 ~ 17:20, Thur, CSIE105)

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:

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

TA: Wei-Ming Chen ( ) , Yen-Hua Huang ( ), Chin-Chi Hsu ( )


General References

  1. Social Network Data Analytics, edited by Charu C. Aggarwal

  2. Social Media Mining and Social Network Analysis: Emerging Research, edited by Guandong Xu

  3. Information and Influence Propagation in Social Networks, Wei Chen, Laks V.S. Lakshmanan, Carlos Castillo

  4. Social Media Mining, Reza Zafarani, Mohammad Ali Abbasi, Huan Liu

  5. Networks, Crowds, and Markets: Reasoning About a Highly Connected World  David Easley, Jon Kleinberg

  6. Network Science: Interactive Textbook László Barabási,

  7. Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman, and Jeff Ullman, 

  8. Social and Economic Networks, Matthew O. Jackson,

Syllabus (tentative)

Date Topic Homework Materials
9/18 Intro to SNA,Static and Dynamic Social Network Properties Aggarwal CH2, Jackson CH2
9/25 Social Network Models hw0 out Liu CH4, Jackson CH4
10/2 Diffusion and information spread Model hw1 out Aggarwal CH7, Wei Chen Ch2, Liu Ch8
10/9 Learning influence spread model Wei Chen Ch7, Liu Ch7
10/16 Link prediction hw1 due Aggarwal CH9
10/23 Node prediction hw2 out Aggarwal CH5
10/30 Paper Presentation 1 ASONAM 2011, KDD2013, ACL2012, ACL 2013, DMKD2013
11/6 Recommendation in social networks Liu Ch9, book 9
11/13 Community Detection hw2 due Liu CH6, Aggarwal CH4
11/20 Location-based Social Network Analysis hw3 out HP. Hsieh
11/27 Mining in Social Media   Aggarwal CH15, Liu CH10
12/4 Integrating sensor and social networks   Aggarwal CH14
12/11 Paper Presentation 2 hw3 due SOLOMO
12/18 Paper Presentation 3 SDM2013, ASONAM2014, WWW2013_Yang, multi-party_inference
12/25 Sampling and Summarization for social networks   Tutorial
1/1 break    
1/8 Final Project Presentation