Intro to Social Networking:


Professor B. Mishra
Rm. 1002, 715 Broadway, New York, NY 10003.
Teaching Assistants:
Pujitha Gade [ email: ]
TA Office Hours: Wed and Friday 2-3PM.(Courant 13th floor student lounge)
Please submit the homeworks in hard copies so that she can return them with her notes on them.

First Day of Class: September 08 2015
Last Day of Class: December 08 2015

Cancelled Classes (tentative):
September 29 and October 01 2015 (Organizer and Lecturer, Como Summer School on Cancer, Causality and Complexity),
December 03 2015 (Invited Talk at BICT 2015, Columbia University, NY),
Note that some of these classes may be covered by Guest Lectures.

Office Hours: Tuesdays 3:30 - 4:30 pm.
Office Phone: 212.998.3464
Email Address:

Day, Time and Place:
Tuesdays and Thursdays, 2:00-3:15pm EST, CIWW 201 (251 Mercer St, NYC).

Credits for Course:

Mathematical Maturity, Programming and Algorithms

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

Home Work [
#1 (due 10/6/15): Ex.2.5 (1, pp 39) & (3. pp40)
[(1, pp44) & (3. pp 46) in here]

#2 (due 10/13/15) (due 10/15/15)
(1) SHOW: For all graphs G, either G or its complement G' is connected.
(2) SHOW: For all graphs G, some two vertices have the same degree.

#3 (due 10/20/15)
(1) SHOW: Ramsey Number R(3) = 6.
(2) SHOW: For p > 0, G(n,p) almost surely contains a triangle, K_3.

#4 (due 10/29/15)
(1) Compute Page Ranks of the pages in figures 14.15 and 14.16 on page 378.
[pp 429-430 in here]
(2) Ex. 14.7 (5, pp 382)
[pp 433 in here]

#5 (due 11/05/15)
(1) Ex. 6.11 (1, pp 179)
(2) Ex. 6.11 (3, pp 180)
[pp 200 and 201 in here]

#6 (due 11/12/15)
(1) Ex. 6.11 (5, pp 180)
(2) Ex. 6.11 (6, pp 181)
[pp 201 in here]

#7 (due 11/19/15)
(1) Ex. 22.11 (1, pp 641)
[pp 730 in here]


Quiz [ ]

Reading Assignment [ ]

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 || Note #14 || Note #15 || Note #16 || Note #17 || Note #18 || Note #19 ]

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, colleagues and acquaintances; amazon, ebay, etc. provide opportunities for trading; uber, airbNb, etc. create platforms for sharing economy; silkroad, agora, etc., create systems for anonymous and often illicit 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 emphasize following topics:

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

(2) Historical developments in computer science, culminating now in ubiquitous social-technological netwroks

(3) Fundamental principles of computer science

(4) Graph Theory and Evolving Social Networks

(5) Graph Laplacians and Social Ranks

(6) Game theory: Information Asymmetric Games and Deception

(7) Signaling and Cyber Security

(8) Digital Market Places

(9) Ad Exchanges

(10) Crypto-Coins and Payment Systems

(9) Case Studies:

Personal Data Markets, Wikileaks, Bit-coins, Cyber Security (M-coins), Information Finance Markets (WealthFront, etc.), Market Microstructure

Fequently Asked Questions (FAQs)

Q1. The course description says, `underlying biology has become obscured, but not dormant,' but the syllabus does not mention how this is going to be brought up. Will the class require prior knowledge of biology?
A1. The course does not require any prior knowledge of biology, nor will any biology be covered in the class in great details. The course will use ideas from evolutionary game theory and its connection to signaling and evolutionary games.

Q2. What is the goal of the class? Will it be: descriptive/prescriptive? qualitative/quantitative? Will it involve algorithms, architectures and simulations?
A2. The intent is to make the class as quantitative and algorithmic as possible, while being descriptive (how current internet works) as well as prescriptive (how the future internet should work). The students will engage in class projects that will involve some implementation and simulation.

Required Text(s):

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

Bud Mishra
April 1 2015