CS 6850 - The Structure of Information Networks

General Information

Jon Kleinberg talking about the theory behind social networks. More formally,

The past decade has seen a convergence of social and technological networks, with systems such as the World Wide Web characterized by the interplay between rich information content, the millions of individuals and organizations who create it, and the technology that supports it. This course covers recent research on the structure and analysis of such networks, and on models that abstract their basic properties. Topics include combinatorial and probabilistic techniques for link analysis, centralized and decentralized search algorithms, network models based on random graphs, and connections with work in the social sciences.

Prerequisites

The course prerequisites include introductory-level background in algorithms, graphs, probability, and linear algebra, as well as some basic programming experience (to be able to manipulate network datasets).

Topics Covered

• Random Graphs and Small-World Properties
• Cascading Behavior in Networks
• Heavy-Tailed Distributions in Networks
• Game-Theoretic Models of Behavior in Networks
• Spectral Analysis of Networks
• Clustering and Communities in Networks

Workload

One programming assignment, one theory assignment, a reaction paper, and a final project. Most of the work is the final project.

General Advice

There isn’t much required work for this class. As a consequence, you will get the most out of this class if you engage yourself during lecture.

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