Course Overview


We will cover topics in network analysis in this course, including social networks and information networks (e.g., the web). We will introduce basic concepts in network theory, metrics to characterize networks, models to explain the generation of networks, and methods to further analyze networks. In the lab sessions, the students will experience various analysis tools to analyze real-world network data. In the second half of the course, we will introduce a wide variety of applications of network analysis to real-world problems such as information retrieval. The final course project will provide students the opportunity to apply the concepts and techniques learned in class to networks of their interest.




校外学生5月21日- 6月25日网上注册并选课、缴费等。


个人信息表 [click here]


Required Text

Wouter de Nooy, Andrej Mrvar, and Vladimir Batagelj. Exploratory Social Network Analysis with Pajek. Structural analysis in the social sciences. New York: Cambridge University Press, 2005. (Pajek) [pdf]

Newman, M. E. J. "The Structure and Function of Complex Networks." SIAM Review. 45 (2003): 167-256.(MEJN) [review]

David Easley and Jon Kleinberg. Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press, Spring 2010.(DEJK) [pdf]
中文版:网络、群体与市场:揭示高度互联世界的行为原理与效应机制,李晓明、 王卫红译,清华大学出版社 (2011-10出版)



R & the igraph package

Find your own ego network on Sina Weibo (account required)


Date: July.30.2012 - August.5.2012; Exam on August.6

Time: 9:00-11:30 AM & 2:30-5:30 FM

Classroom: 北京大学理科二号楼2736


Introduction to the course Video  Slides

May.25.2012 12:00PM - 12:50 PM, classroom 1126

Day 1

Lectures: introduction; basic concepts

Discussion session 1: get to know each other

Discussion session 2: get familiar with pajek

Homework 1 released

Reading:MEJN Section 1-3 | Pajek Chapter 1| DEJK Chapter 1 | Pajek Chapter 2 | DEJK Chapter 2

Day 2

Lectures: centrality/prestige; small world networks

Discussion session:
What is the good metric of centrality in real world scenarios, such as Twitter or Weibo?
Centrality = influence?
How to maximize centrality or influence?

Reading: Baker et al. 1993 | Friedkin et al., 1993 | Pajek Chapter 6 & 9 | MEJN Section 6 | DEJK Chapter 20 | Watts & Strogatz 1998 | Travers et al., 1969

Day 3

Lectures: scale free networks; network evolution

Team challenge:
Choose an interesting real network, collect it and visualize it. Show others what you find!

Homework 1 due

Homework 2 released

Reading: Adamic | MEJN Chapter 7 | DEJK Chapter 18 | Barabasi et al., 1999 | Guimera et al., 2005 | Leskovec er al., 2005 | Backstrom et al., 2006

Day 4

Lectures: visualization; search;

Panel: Is it a game of winner-takes-all (i.e., Facebook becomes the emperor/dictator of social media)? How do you imagine the future of social media?

Guest Lecture: "From Text and Data to Knowledge Base - Social Semantic Web in Action" (Mark Greaves & Jesse Wang, from Vulcan Inc. 3:30PM - 5:00 PM) intro | slides

Reading: Kleinberg 2003 | Watts et al. 2002 | Adar 2002 | Liben-Nowell et al. 2005 | Adamic et al. 2005

Day 5

Lectures: ranking; classification and recommendation

Homework 2 due

Homework 3 released

Reading: DEJK Chapter 13 & 14 & 19 | Doyle et al. | Golder et al., 2006 | Menczer et al., 2005 | Mei et al., 2008 | Erkan et al., 2004

Day 6

Lectures: communities; diffusion;

Discussion session: project teaming and discussion

Reading: Girvan & Newman, 2002 | Feld 1982 | Adamic et al. 2003 | Frey & Dueck 2007| Pajek Chapter 3 & 5 | DEJK Chapter 3, 16 & 19

Day 7

Lectures: network resilience; wrapping up

Discussion session 1: project proposals and QA

Brain storming: future of social networks

Homework 3 due

Reading: Albert et al. 2000 | Cohen et al. 2000 | Callaway et al. 2000

Course Evaluation

Class participation: 15%

Homework assignments: 30%   Homework 1 | Homework 2 | Homework 3

Exam: 30% (open book) Exam (due by Aug 10)

Course project: 25%: Proposal 5% (due by the end of the lectures) + Final report (due Aug. 20th)