This course provides an overview of quantitative methods appropriate for the data analysis of social and economic networks. Many social and economic activities are embedded in networks because datasets with graph theoretic structure are increasingly available to practical users. Two of the main goals are to study:
Theoretical/statistical/mathematical/computational models and their applications with respect to social and network data from development and labor economics are studied. Essential topics/case-studies are selected from, but not limited to, network formation, peer effects and the social multiplier, social capital and trust, information aggregation in networks, social learning, trading in networks, technology diffusion, job search, and other related topics.
This course assumes no prior experience with programming.
The text/references is/are available at the CUHK library.
The text/reference should not be treated as a substitute for the lectures. The lectures may present the material covered in the text in a different manner, or deviate from it entirely. You should take your own notes in class.
The Chinese University of Hong Kong places very high importance on honesty in academic work submitted by students, and adopts a policy of zero tolerance on cheating and plagiarism. Any related offence will lead to disciplinary action including termination of studies at the University. For information on categories of offenses and types of penalties, students should consult the following link: .
Your final letter-grade will be determined by your point Ranking viz. your final score (out of 100 points). The total score for your course grades is distributed as follows:
| Sunday | Monday | Tuesday | Wednesday | Thursday | Friday | Saturday |
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | ||||
| 4 | 5 - Lecture 1 | 6 | 7 | 8 | 9 | 10 |
| 11 | 12 | 13 | 14 | 15 | 16 | 17 |
| 18 | 19 - Lecture 2 | 20 | 21 | 22 | 23 | 24 |
| 25 | 26 - Lecture 3 | 27 | 28 | 29 | 30 |
| Sunday | Monday | Tuesday | Wednesday | Thursday | Friday | Saturday |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | 3 - Lecture 4 | 4 | 5 | 6 | 7 | 8 |
| 9 | 10 - Lecture 5 | 11 | 12 | 13 | 14 | 15 |
| 16 | 17 - Lecture 6 | 18 | 19 | 20 | 21 | 22 |
| 23 | 24 - Lecture 7 - Midterm Test | 25 | 26 | 27 | 28 | 29 |
| 30 | 31 - Lecture 8 |
| Sunday | Monday | Tuesday | Wednesday | Thursday | Friday | Saturday |
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| 6 | 7 - Lecture 9 | 8 | 9 | 10 | 11 | 12 |
| 13 | 14 - Lecture 10 | 15 | 16 | 17 | 18 | 19 |
| 20 | 21 - Lecture 11 | 22 | 23 | 24 | 25 | 26 |
| 27 | 28 - Lecture 12 | 29 | 30 |
| Sunday | Monday | Tuesday | Wednesday | Thursday | Friday | Saturday |
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | ||||
| 4 | 5 - Final Test | 6 | 7 | 8 | 9 | 10 |
| 11 | 12 | 13 | 14 | 15 | 16 | 17 |
| 18 | 19 | 20 | 21 | 22 | 23 | 24 |
| 25 | 26 | 27 | 28 | 29 | 30 | 31 |
There will be three graded homework assignments.
Please note that you MUST do the whole homework entirely by yourself. In case of difficulty, you may consult the instructor and the tutors during their office hours. Any answers that show evidence of having been done with others will receive a score of zero; stronger action may also be taken (visit ). Don’t copy the work of others! Be neat, concise and well-organized.
Late homework answers will NOT be graded, and will receive a score of zero.
Submit your homework using Gradescope in Blackboard.
Once you have enrolled your course, we will send you a username and password to access your online learning resources.