i3 Workshop on Network Science: A Short Introduction
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Overview
This mini workshop explores networks as a primary metaphor and mechanism for a variety of information-related phenomena. The advancement of interconnected information and communication technologies has made networks one of the dominant ways of analyzing the use and flow of information among individuals, institutions, and societies. The course starts with the basics of graph theory and moves to studying network structures and how they emerge through various network models. We begin with the traditional random graph model and we move to the more realistic, socially-inspired models of small-world and preferential attachment. We will further explore processes in a network such as diffusion of epidemics and learning.
Learning outcomes
Upon satisfactory completion of this course, the i3 scholars will:
- understand basic metrics that describe the various properties of a network
- become familiar with "universal" structural properties observed in a large number of networks
- learn the very basics of the random network model, the small-world network model and the preferential attachment model
Preparation
If you want to perform the steps for the demos I will be presenting bring your laptop with the R software installed. Specific instructions for different platforms can be found here for: Windows, Mac and Linux. We will also be using the library igraph, which you can download and install as described here.
Basic Information
Workshop Outline
Materials
Links
Basic Information
Instructor: Konstantinos Pelechrinis (kpele AT pitt.edu)
Office: IS Building, 717B
Lectures:
Wednesday, June 11th, 10:00 - 11:30 am - IS 305
Textbook:
- G. Caldarelly and M. Catanzaro, Networks:A Very Short Introduction, Oxford University Press, ISBN 978-0-19-958807-7, 2012.
Workshop Outline
- Graph Theory Basics
- Basic definitions, node degrees, local clustering, paths, components.
- "Universal" network properties
- Giant components, fat-tail degree distributions, 6-degrees of separation, high clustering.
- Network Models
- Random networks
- Small-World
- "Rich-gets-richer" - Preferential attachement
- Dynamic Processes in Networks (if time permits)
Materials
Links