Incentives and Information in Networks


Instructor: Yaron Singer
Time: M./W. 10-11:30 am
Room: Pierce Hall 100F

Staff: We're fortunate to have Bo Waggoner (bwaggoner at fas at harvard dot edu) as our teaching fellow in this course.

Overview: For several decades there has been a broad interest in the way in which information spreads through a society. With the recent exploding adoption of social networking services, information diffusion through networks is becoming easier to analyze and predict, as well as engineer. In this course we will focus on algorithms for acquiring, disseminating, and learning information in social networks. We will discuss models, algorithms, and incentives-based mechanisms, all designed for predicting and engineering information processes in social networks. The material will draw upon topics in sociology, theoretical computer science, probability theory, machine learning, and microeconomics.

Prerequisites: This will be a mathematically rigorous course, but the only prerequisite is mathematical maturity. Prior coursework in algorithms, game theory, and probability, as well as a good personality will be helpful but not necessary. There may also be some simple programming exercises. The course is intended for graduate students, but advanced undergraduates are encouraged to attend as well.

Goals: The goal of this course is to provide an introduction to social networks, and expose the frontier of research on algorithmic topics in this exciting area. Along the way we will cover topics in approximation algorithms, game theory and mechanism design, probability and combinatorics, and some statistical machine learning.

Assessment: The main component of this course will be a research project. This project can be data-oriented or theoretically-focused, or (better) a combination of both. There is room for projects involving algorithms, data mining, machine learning, game theory and mechanism design, natural language processing, privacy, probability, and statistics. There will also be presentations by students on recent research papers, and some guest lectures by authors of some of the papers we will discuss.

Topics Covered:
  1. The structure of social networks
  2. Information acquisition in networks
  3. Information diffusion in networks
  4. Marketing through networks
  5. Learning in networks