This lab will introduce you to network analysis. You will analyze several types of networks, including your own social network.
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.
The lab has three components: 1. Centrality Measures, 2. Social Networks, and 3. Directed Networks and Social Hierarchy.
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.
This component shows how to analyze social network by finding communities and bridges, by measuring connectivity, and by analyzing your own social network.
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.
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- Watts, D. (2016). "How small is the world, really?" Blog post, medium.com.
- Merten, D. E. (1997). "The meaning of meanness: Popularity, competition, and conflict among junior high school girls." Sociology of Education.
- Eckert, P. (1989). "The corporate structure of the school." In Jocks and Burnouts. Teacher's College Press.
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