nreichen / CSSLabs-Networks

Introduction to network analysis and visualization

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User Guide for CSS Lab: Networks

This lab will introduce you to network analysis. You will analyze several types of networks, including your own social network.

Technical Requirements

This lab uses the vis javascript library to create interactive network visualizations. Currently, only Jupyter Notebook is able to support these visualizations, so this lab should be run using jupyter notebook and not jupyter lab. To use Jupyter Notebooks on Azure Notebooks, open the notebook and select the "Help" > "Launch Classic Notebook" menu item.

Notebooks

The lab has three components: 1. Centrality Measures, 2. Social Networks, and 3. Directed Networks and Social Hierarchy.

Centrality Measures

This component shows how real-world networks can be represented abstractly as a set of nodes connected by edges, and how this representation can be useful. It covers measures of prominence, including: degree, betweenness, and eigenvector centrality. This component also covers visualization, community structure and shortest paths.

Network Structure

This component shows how to analyze social network by finding communities and bridges, by measuring connectivity, and by analyzing your own social network.

Directed Networks and Social Hierarchy

This component demonstrates analysis of a directed network of friendships among teenagers in a high school. It covers methods for inferring social status from reciprocated and unreciprocated friendships. Changes in status are analyzed over time and compared with demographics and behaviors such as substance use.

Suggested Readings

Centrality Measures

Social Networks

Directed Networks and Social Hierarchy

About

Introduction to network analysis and visualization


Languages

Language:Jupyter Notebook 78.4%Language:MATLAB 18.1%Language:Python 3.5%Language:CSS 0.1%