Social Networks:

CSCI-GA.3033-002


Lecturer:
Professor B. Mishra
with
Teaching Assistants:
Vince Zhu [ email: vince.zhu@nyu.edu ]

Calendar
First Day of Class: January 24 2017
Last Day of Class: May 02 2017

Cancelled Classes (tentative):
Note that some of these classes may be covered by Guest Lectures.


Office Hours: By appt.
Office Phone: 212.998.3464
Email Address: mishra@nyu.edu

Day, Time and Place:
Tuesday, 7:10-9:00pm EST, CIWW 201 (251 Mercer St, NYC).

Credits for Course:
3

Prerequisites:
Mathematical Maturity (Discrete Mathematics and Linear Algebra), Programming and Algorithms

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

Home Work [

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Talks [ ..|:|.. ]

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

Reading Assignment [
Lecture Number #1: Chapter 1 (Overview, pp 1-17) ||
Lecture Number #2: Chapter 2 (Graphs, pp 21-41) ]

Notes [ ]


Syllabus:
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 emphasize following topics:

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

(2) Graph Theory and Social Networks

(3) Graph Laplacians and Social Ranks

(4) Game theory: Information Asymmetric Games and Deception

(5) Communication and Signaling

(6) Digital Market Places

(7) Ad Exchanges

(8) Crypto-Coins and Crypto-Markets

(9) Case Studies:

Personal Data Markets, Wikileaks, Bit-coins, Cyber Security (M-coins), Information Finance Markets (StockTwits, Quantopia, Wealth Front, 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.
Homework(s):
Class Presentation.

Bud Mishra
September 1 2017