CAP 6315: Social Networks and Big Data Analytics

Announcement

(The content of this page changes frequently)

Class Time: 2020 Spring, 11:00AM - 12:20PM (T, TR)
Classroom: 2020 Spring, CM-130
Office Hour: 2020 Spring, EE-503B, 9:30AM - 11:00AM (T, TR)

Textbook:

Social Media Mining: An Introduction , R. Zafarani, M. Abbasi, and H. Liu, Cambridge University Press, 2014 (E-copy available online)

Reference books:

Community Detection and Mining in Social Media, Morgan & Claypool Publishers, 2010 (E-copy available in Library)
Statistical Network Analysis with igraph, Springer (Online version available)
Networks, Crowds, and Markets: Reasoning About a Highly Connected World (Online version available)
Introduction to social network methods (Online version available)
The developer's guide to social programming Addison-Wesley, 2010

Other Useful Resources:

UC Berkeley, School of Information: Analyzing Big Data with Twitter
UMass, Dept. of Electrical and Computer Engineering: Online Social Networks
UCSB, Dept. of Computer Science: Online Social Networks
Hong Kong UST, Dept. of Computer Science & Eng.: Social Networks, Media and Transfer Learning

Course Description: This course teaches students basic concepts of big data analytics, with an application in social network analysis. The class will cover three major topics including big data analytics platform, MapRedue programming, and social network analytics. Detailed topics include MapReduce based computing framework, general algorithms for data analytics, trend and outbreak detection from social network streams. The lectures will include practical sessions dedicated to the implementation of big data analytics with selected programming language and tools.

Topics:

Social Networks and Mining Algorithms

Graph Theory and Social Network Characteristics
General Graph Mining Algorithms
Community Detection
Social Influence
Link Prediction and Trend Modeling
Social Network Data Characteristics

R Programming for Social Network Analysis

R Programming for Social Network Visualization
R Programming for Social Network Node and Link Analysis
R Programming for Social Network Community Detection

Big Data Analytics

Introduction to MapReduce
MapReduce Programming Model
Social Network Mining Algorihtms and MapReduce

Lectures, Assignments, and Projects

Course Schedule by Week

Resources

Homeworks

Dataset

Project

Grading policy:

Homework	40
Mid-term	15
Term project	15
Student Presentation	10
Final or Research Report	15
Participation	5

Your final grade will be based on the scores you have earned from the above categories (compared to the performance of other students in the class).

For the term project and the final course report, students will form groups to work on some identified research projects (after discussion with the instructor), investigate new social netowrk analysis designs, and carry out implementations and report the validations and final outcomes in the report. The report should follow the research publication format, including abstract, introduction, design, experiments, conclusion and reference.

Late policy:

All assignments are due midnight on the assigned due date. Please refer to the Assignments and Projects for details on submission. Late submission is allowable, however, the late penalty is -2pts/day.

Communication:

All important course communication will be done using your fau.edu email address. Sending email to me from another account is disencouraged, and if you do you must set the reply-to field to your FAU email account if the message concerns grading or evaluation in any way. You must also include your name in all messages concerning the course. If you have your FAU email forwarded to an AOL or other email account, read this important notice concerning blocking of FAU email.

All work in this course must be INVIDUAL effort unless specified otherwise.