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CSCI5070 Advanced Topics in Social Computing

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Extra Credit Assignments

20012-13 Term 1

Lecture I Lecture II Tutorial I Tutorial II
Time T2-4, 9:30 am - 12:30 pm T9, 4:30 pm - 5:30 pm
Venue NAH 8 LSK 308

The Golden Rule of CSCI5070: No member of the CSCI5070 community shall take unfair advantage of any other member of the CSCI5070 community.

Course Description

This course introduces fundamental as well as applied computational techniques for collaborative and collective intelligence of group behaviours on the Internet. The course topics include, but are not limited to: web intelligence, web data mining, knowledge discovery on the web, web analytics, web information retrieval, learning to rank, ranking algorithms, relevance feedback, collaborative filtering, recommender systems, human/social computation, social games, opinion mining, sentiment analysis, models and theories about social networks, large graph and link-based algorithms, social marketing, monetization of the web, security/privacy issues related to web intelligence and social computing, etc.

Learning Objectives

Learning Outcomes

Learning Activities

  1. Lectures
  2. Tutorials
  3. Web resources
  4. Videos
  5. Quizzes
  6. Examinations

Personnel

Lecturer Tutor Tutor
Name Irwin King Baichuan Li
Email king AT cse.cuhk.edu.hk bcli AT cse.cuhk.edu.hk
Office Rm 908
Telephone 3943 8398
Office Hour(s) * M8, Monday 3:30 pm - 4:30 pm

* T8, Tuesday 3:30 pm - 4:30 pm

Note: This class will be taught in English. Homework assignments and examinations will be conducted in English.

Syllabus

The pdf files are created in Acrobat 6.0. Please obtain the correct version of the Acrobat Reader from Adobe.

Week Date Topics Tutorials Homework & Events Resources
1 11/9 Introduction to Web Intelligence and Social Computing
Web 2.0

01-Introduction.pdf
Tim O'Reilly on Web 2.0, The Economist, 20/3/2009
2 18/9 Graphs

02-Graphs.pdf
Python & R

CSCI5070-01-Tutorial.pdf
Project Specs and template

1. Proposal specs
2. Proposal template
Introduction to Social Networks
3 25/9 Social Network Theory I

02-Graphs Additions.pdf
03-Networks.pdf
Crawler

02-Crawler.pdf
Assignment 1
4 2/10 Public Holiday Public Holiday Generating Random Graphs
The Clique Algorithm
5 9/10 Social Network Theory II

04-GraphMining and Link Analysis.pdf
04-Networks.pdf
NetworkX and Graphviz 03-NetworkX & Graphviz Introduction to Information Retrieval
6 16/10 Recommender Systems I

05-Recommender 1.pdf
Link Analysis and Memory-based Collaborative Filtering
Link Analysis & Memory-based CF
7 23/10 Public Holiday Public Holiday Assignment 2
8 30/10 Recommender Systems I

05-Recommender 1.pdf
Assignment 1 Solution
9 6/11 Recommender Systems II

06-Recommender 2.pdf
Assignment 2 Solution
10 13/11 Midterm Examination Introduction to Latex
11 20/11 Opinion Mining and CQA

1. 07-CQA.pdf
2. 07-QA & Deep QA.pdf
3. 07-Sentiment Analysis.pdf
Sentiment Analysis in Practice Report Template
12 27/11 Human Computation & Crowdsourcing

1. 08-Crowdsourcing.pdf
Midterm Review
13 4/12 Wrap Up

Project Presentations
EduTech on Social Computing in Education

Class Project

Class Project Presentation Schedule

Class Project Presentation Requirements

  1. For each group, the total time for the presentation is 15 minutes, including 12 minutes for the talk and 3 minutes for Q&A. The presentation will follow the order above. Since this class will last until the end of all the presentations, if the time is not suitable for you, you can tell us to change your order.
  2. In the presentation, there is no demo part. The demo part is an independent process divided into two sub-sections. The first section will be hold in tutorial time on Dec. 1st. In this section, all the groups should demo your program to the two tutors. The tutors will guide you to revise your program. The second section will be hold on Wednesday, Dec. 16th. In this section, Prof. King will check your program before the final submission of your codes.
  3. For groups implementing graphical algorithms, you should explain one algorithm as detailed as you can in the presentation. You should give an example with the structure of nodes, values, and your calculations. You also need to analyze the complexity of your algorithms and test whether your algorithms can be applied in large graphs. For other groups, you should focus on three aspects including the motivation of your idea, the detailed algorithms, and the justification of your methods comparing to naive methods through experiments.

Examination Matters

Examination Schedule

Time Venue Notes
Midterm Examination
Written
TBA TBA TBA
Midterm Examination
Programming
TBA TBA TBA
Final Examination TBA TBA TBA

Written Midterm Matters

  1. The midterm will test your knowledge of the materials.
  2. Answer all questions using the answer booklet. There will be more available at the venue if needed.
  3. Write legibly. Anything we cannot decipher will be considered incorrect.
  4. One A4-sized cheat-sheet page.

Grade Assessment Scheme

Homework
Assignments
Project Report Project Presentation Final Examination
20% 20% 10% 50%
  1. Assignments (30%)
    1. Written assignments
    2. Optional quizzes
  2. Midterm Examination (30%)
  3. Project (40%)
    1. Report (20%)
    2. Presentations (20%)
  4. Extra Credit (There is no penalty for not doing the extra credit problems. Extra credit will only help you in borderline cases.)

Required Background

  1. Pre-requisites
    1. - CSC 1110 or 1130 or its equivalent. (Not for students who have taken CSC 2520).

Reference Books

FAQ

  1. Q: What is departmental guideline for plagiarism?
    A: If a student is found plagiarizing, his/her case will be reported to the Department Discipline Committee. If the case is proven after deliberation, the student will automatically fail the course in which he/she committed plagiarism. The definition of plagiarism includes copying of the whole or parts of written assignments, programming exercises, reports, quiz papers, mid-term examinations. The penalty will apply to both the one who copies the work and the one whose work is being copied, unless the latter can prove his/her work has been copied unwittingly. Furthermore, inclusion of others' works or results without citation in assignments and reports is also regarded as plagiarism with similar penalty to the offender. A student caught plagiarizing during tests or examinations will be reported to the Faculty Office and appropriate disciplinary authorities for further action, in addition to failing the course.

Resources

Social Networks-Theory Graph Theory

Graph Mining

Link Analysis

Learning to Rank

Recommender Systems

Q & A

Human Computation/Social Games

Opinion Mining/Sentiment Analysis

Visualization

Programming

Midterm Evaluation Sign-up Sheet

Midterm Project Evaluation AM
Real name9:309:4510:0010:1510:3010:4511:0011:1511:3011:45
Sze-To Ho Yin, Wong Pak Kan, Cheung Kwan Yau    2012-10-30 13:32:17     
Zeng Xingyu, Shao Jing, Yi Shuai, Wang Zhe     2012-10-30 14:39:00    
Liu Lixin, Wang Shuai, Zhang Haoran      2012-10-30 16:17:21   
Zhu Jieming, Wu huanhuan, Hu Xihao       2012-10-30 19:28:14  
HU Pili  2012-11-06 11:20:17       
Jiang Dailin, Zhang Chunsheng, SUN Jingxian   2012-11-06 17:54:49      
Zhao Shenglin, Zhang Hongyi         2012-11-05 16:53:24
GAO Jiayang, Man Yuanyuan, ZHANG Yin        2012-11-06 11:09:31 
Shi Jianping, Xia Yang 2012-11-06 13:40:54        
Test<a href="#">Noname</a>2012-11-12 18:50:47         
ZAP2018-02-10 23:55:262018-02-10 23:55:262018-02-10 23:55:262018-02-10 23:55:262018-02-10 23:55:262018-02-10 23:55:262018-02-10 23:55:262018-02-10 23:55:262018-02-10 23:55:262018-02-10 23:55:26
 2222222222
Midterm Project Evaluation PM
Real name12:3012:451:001:151:301:452:002:152:302:453:00
Wang Jiexin    2012-10-30 17:33:15      
Liao Wenchong, Zhang Linyun, Yao Jianliang   2012-10-30 19:16:52       
Liu Lili, Yang Furu, SongShuang          2012-10-30 20:31:31
Chan Chun Wing, Ding Qian, Li Yan Kit, Tang Chung Pan     2012-11-06 11:11:24     
BAO Qing, LIU Kai         2012-11-05 18:33:10 
David Kwan, Chan Kwun Man       2012-11-02 13:55:28   
Irwin King2012-11-06 11:16:052012-11-06 11:16:052012-11-06 11:16:05        
Huang Qun, Liu Qin      2012-11-06 11:12:16    
Li Runhui, Xu Silei        2012-11-06 11:13:12  
ZAP2018-02-10 23:55:262018-02-10 23:55:262018-02-10 23:55:262018-02-10 23:55:262018-02-10 23:55:262018-02-10 23:55:262018-02-10 23:55:262018-02-10 23:55:262018-02-10 23:55:262018-02-10 23:55:262018-02-10 23:55:26
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Final Project Presentation Sign-up Sheet

Final Project Presentation Session 1
Real name9:009:159:309:4510:0010:1510:3010:4511:00
Irwin King        2012-11-19 15:31:26
BAO Qing, LIU Kai    2012-11-19 17:04:06    
Chan Chun Wing, Ding Qian, Li Yan Kit, Tang Chung Pan       2012-11-19 18:26:00 
Zeng Xingyu, YI Shuai, Shao Jing, Wang Zhe     2012-11-19 18:39:32   
HU Pili      2012-11-19 20:29:49  
GAO Jiayang, Man Yuanyuan, ZHANG Yin   2012-11-20 09:40:44     
Sze-To Ho Yin, Wong Pak Kan, Cheung Kwan Yau  2012-11-20 13:23:46      
ZHAO Shengling, ZHANG Hongyi 2012-11-27 11:22:49       
ZAP2018-02-10 23:55:272018-02-10 23:55:272018-02-10 23:55:272018-02-10 23:55:272018-02-10 23:55:272018-02-10 23:55:272018-02-10 23:55:272018-02-10 23:55:272018-02-10 23:55:27
 122222222
Final Project Presentation Session 2
Real name11:1511:3011:4512:0012:1512:3012:4513:0013:1513:30
Wang Jiexin   2012-11-19 21:01:15      
LIU Qin, HUANG Qun2012-11-19 21:04:58         
Li Runhui, Xu Silei 2012-11-19 21:06:06        
Shi Jianping, Xia Yang         2012-11-19 21:08:56
Liu Lili, Yang Furu, Song Shuang       2012-11-19 21:16:52  
Jieming Zhu, Huanhuan WU, Xihao Hu  2012-11-19 23:11:35       
Lixin Liu, Haoran Zhang, Shuai Wang     2012-11-20 09:57:55    
David Kwan, Chan Kwun Man    2012-11-20 15:23:50     
Liao Wenchong,Zhang Lingyun, Yao Jianliang        2012-11-20 16:33:14 
SUN Jingxian, ZHANG Chunsheng, JIANG Dailin      2012-11-20 17:17:12   
ZAP2018-02-10 23:55:282018-02-10 23:55:282018-02-10 23:55:282018-02-10 23:55:282018-02-10 23:55:282018-02-10 23:55:282018-02-10 23:55:282018-02-10 23:55:282018-02-10 23:55:282018-02-10 23:55:28
 2222222222