CS 249: Special Topics - Mining Information and Social Networks

Instructor: Yizhou Sun

• Office hours: M/W 1:00pm-2:00pm

Lecture times: M/W 10am-11:50am
Lecture location: PAB Room 1749

About the Course

Information and social networks including World Wide Web, Facebook, Twitter, Weibo, Forum network, Citation network, Game network, Movie network, and Medical network now become a very important and ubiquitous data type. Which patterns can be defined on such data? What kind of prediction can be made? Which models and algorithms can be used to deal with networked data? How can we handle large-scale networked data? All these issues will be discussed in this course.

The goal of the course is to learn the most cutting-edge topics, models and algorithms in information and social network mining, and to solve real problems on real-world large-scale information/social network data using these techniques. Students are expected to read and present research papers, and work on a research project related to this topic.

Syllabus (Schedule)

1.      Introduction and Basics of Information/Social Networks
2.      Clustering / Community Detection
3.      Classification / Label Propagation
4.      Similarity Search
5.      Network Embedding
6.      K-Core Subgraph Decomposition and Its Applications
7.      Diffusion and Influence Maximization
8.      Recommendation

Prerequisites

• There is no official pre-requisite for this course.
• The students are expected to have knowledge in data structures, algorithms, basic linear algebra, and basic statistics. It is highly recommended that you have already had some background knowledge in data mining and machine learning. You will also need to be familiar with at least one programming language, and have programming experiences.

Grading

• Paper presentation and homework: 40%
• Research  project: 50%
• Participation: 10%

*Note: all the deadlines are 11:59PM (midnight) of the due dates.

 Q & A

This term we will be using Piazza for class discussion. The system is highly catered to getting you help fast and efficiently from classmates, the TA, and myself. Rather than emailing questions to the teaching staff, I encourage you to post your questions on Piazza.

Tips: Answering other students' questions will increase your participation score.

Find our class page at: https://piazza.com/ucla/winter2017/comsci2492/home

Academic Integrity Policy

"With its status as a world-class research institution, it is critical that the University uphold the highest standards of integrity both inside and outside the classroom. As a student and member of the UCLA community, you are expected to demonstrate integrity in all of your academic endeavors. Accordingly, when accusations of academic dishonesty occur, The Office of the Dean of Students is charged with investigating and adjudicating suspected violations. Academic dishonesty, includes, but is not limited to, cheating, fabrication, plagiarism, multiple submissions or facilitating academic misconduct."

For more information, please refer to the guidance.