Cancelled Classes (tentative):
Note that some of these classes may be covered by Guest Lectures.
#1 (due 3/7/16): Ex.2.5 (1, pp 39) & (3. pp40)
[(1, pp44) & (3. pp 46) in here]
#2 (due 4/4/16)
(1) SHOW: Ramsey Number R(3) = 6.
(2) SHOW: For p > 0, G(n,p) almost surely contains a triangle, K_3.
#3 (due 4/25/16)
(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]
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
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.