Session 1: Introduction to network analysis
In this session, we cover how to install R and RStudio, how to subset and manipulate data sets, and how to visualize simple networks using the packages: ggraph and igraph.
Corporate strategy in a network perspective
The course is an obligatory course for 3rd year bachelor students at Copenhagen Business School. You can read more about the course here.
Contact and office hours #
You are welcome to contact me via e-mail or to book a 20 min slot during my office hours. Please just select the day and time in the widget below.
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Name: Alexander Gamerdinger
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E-mail: aga.ioa@cbs.dk
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Office: KL 3.43 (3rd floor at Kilen)
Trouble shooting #
- Problemer med at installere tidyverse pakken (https://cbscanvas.instructure.com/courses/30857/discussion_topics/108286)
Sessions #
Sessions will be uploaded continuously…
Session 2: Network components, density and paths
In this session, you will learn how to select components of a network, and how to calculate network density, shortest paths, and the diameter of a network.
Session 3 - Collecting network data
In this session, You will learn how to subset the data set den17 by tags, and how to collect network data from Orbis. Last, we cover how to visualize a two-mode network and to add network attributes
Session 4 - Analysis of node centrality measures
The focus of this session is on node centrality measures. We will cover several metrics including degree centrality, betweenness centrality, closeness and eigenvector centrality.
Session 5 - Brokerage and assortativity
This session will focus on two topics. First, we will look at Burts constraint as a measure for brokerage. Second, we will cover assortativity as a measure of homophily in a network.
Session 6 - Communities and cliques
This session will focus on the Louvain community detection algorithm and the visualization of communities in a network. We will also discover what cliques are, and how they differ from communities.
Session 7 - Visualization
This session will focus advanced network visualizations with the ggraph() package. This will be the last session of this course.