Social Networks:


Professor B. Mishra
Teaching Assistants:
Yangchen Zhang [ email: ]

First Day of Class: January 28 2014
Last Day of Class: May 06 2014

Cancelled Classes (tentative):
February 11 2014 (ICDCIT 2014 Meeting in India)
March 18 2014 (Spring Recess),
April 15 2014 (Privacy Meeting in Italy).
Note that some of these classes may be covered by Guest Lectures.

Office Hours: By appt.
Office Phone: 212.998.3464
Email Address:

Day, Time and Place:
Tuesdays, 5:10-7:00pm EST, CIWW 317 (251 Mercer St, NYC).

Credits for Course:

Mathematical Maturity, Programming and Algorithms

Grading Policy:
Quiz: 55 %; Project: 35 %; Final Exam: 10 %

Home Work [ Home Work 1 | Solution to Home Work 1 || Home Work 2 | Solution to Home Work 2 || Home Work 3 | Solution to Home Work 3 ]

Quiz [ ]

Talks [ ..|:|.. ]

Technology & Courage (Sutherland)
Startups (Videos to Watch)
Business Model Canvas

Notes [ Note #1 || Note #2 || Note #3 || Note #4 || Note #5 || Note #6 || Note #7 || Note #8 || Note #9 || Note #10 || Note #11 || Note #12 || Note #13 ||.. ]

Social Networks is a specific example of many forms of networks that have become ubiquitous in our modern society. Their utilities have been enhanced by their ability to generate massive amount of personal data that need to be analyzed and disseminated quickly. The World Wide Web enables information flows among vast number of humans; facebook, LinkedIn, etc. connect small groups of friends; amazon, ebay, etc. provide opportunities for trading, etc. These networks determine our information, influence our opinions, and shape our political attitudes.

They also link us, often through important but weak ties, to other humans. Their origin is biological: going back to quorum-sensing, swarming, flocking, social grooming, gossip, etc. Yet, as we have connected our social networks to traditional human institutions (markets, justice systems, education, etc.) through new technologies, the underlying biology has become obscured, but not dormant.

This course will introduce the tools, analytics and algorithms for the study of networks and their data. It will show how certain common principles permeate the functioning of these diverse networks: e.g., issues related to robustness, fragility, and interlinkages etc. The lectures will follow the materials in Hopcroft and Kannan's "Foundation of Data Science," with an emphasis on following topics:

(1) Introduction to networks (Biological, Social, Economic and Communication)

(2) Graph theory and social networks

(3) Random graph models

(4) Graph Laplacians and Social Ranks

(5) Data Analytics and Related Algorithms

(6) Game theory

(7) Communication and Signaling

(8) Digital Market Places

(9) Profiling, Privacy, Pricing and Coase Theorem

(10) Eneterpreneurship (Lean Structures, Business Model Canvas, Hypotheses Testing with MVPs)

(11) Case Studies:

Personal Data Markets, Wikileaks, Bit-coins, Cyber Security (M-coins), Information Finance Markets (StockTwits, Quantopia, Wealth Front, etc.), Market Microstructure

Required Text(s):

Recommended Text(s):

Midterm Date:
No Midterm.
Final Date:
Class Project.
Class Presentation.

Bud Mishra
September 1 2003