COURSE
BENEFITS:
PROFESSOR
CHINGYUNG LIN:
ChingYung Lin
is the IBM Lead of the Social and
APPLICABLE
DEGREE PROGRAMS:
COURSE FEES:
Lecturer/Manager: 
ChingYung Lin 




Day & Time
Class 
Monday 7:00pm  9:30pm 

Location: 
Mudd 227 

Credits for course: 
3 

Class Type: 
Lecture 

Prerequisites: 
Basic courses in
probability & statistics such as E3658 (Probability) and signal
processing such as E3801 (Signals and Systems). Students need to know at
least one programming language or statistical tool (Matlab,
C, Java, C++, Perl, Python, SPSS, R) for finishing homeworks. Students outside the 

Description: 
Network as a new
scientific discipline is emerging. Entities  people, information, societies,
nations, devices  connect to each other and form all kinds of intertwined
networks. Socialinformatic networks are becoming
part of our daily life. How do we analyze these “Big Network
Data”? Researchers from multiple
disciplines  electrical engineering, computer science, sociology, public
health, economy, management, politics, laws, arts, physics, math, etc –
are interacting with each other to build up common grounds of network science.
Network theories are being formed for describing the dynamics, behaviors, and
structures. A systematic mathematical formalism that enables predictions of
network behavior and network interactions is also emerging. Transdisciplinary
approaches are usually required to lay the foundations of this science and to
develop the requisite tools. Network data analysis will
become essential for graduate or senior students pursuing advanced
understanding in Signal Processing. For its interdisciplinary nature, it is
also suitable for students in other disciplines conducting researches in
various forms of networks. Students shall learn
fundamental network analysis concept, math, and tools, and gain handon
experiences. Students will need to choose final project topic of their
interest and conduct comprehensive study or preliminary research. 

TA: 
XiaoMing Wu


Required Text(s): 
Textbook: 

Reference Text(s): 
class notes, and reference papers will be available at http://www.ee.columbia.edu/~cylin/course/netsci/NetSci_syllabus.html 

Homework(s): 
four assignments including analytical questions and programming 

Project(s): 
one final project in which students may conduct researches of network science or conduct surveys of emerging techniques. Team collaboration is encouraged. 

Paper(s): 
report of the final project. oral presentation of the final project results required for onsite students. 

Midterm Exam: 
None 

Final Exam: 
None 

Grading: 
Homework assignments 50%, Final Project 50% 

Hardware 
PC with Internet access. 

Software 
Students may use their preferred software (C, C++, Java, Matlab, R, SPSS) on their computers to complete homework assignments. 

Homework 
by email to TA 

Course Outline 

