8.3 Analyzing Ego-Centric Networks
In the textbook, the author explores a range of measures that we’ve introduced when studying complete networks, such as density and centrality. This is another chance to examine these concepts even though computing these measures is the same mathematically for complete or ego networks.
In this week, I encourage you to use a complete-network dataset you have in hand (e.g., your own data, demo data we used in earlier weeks, Twitter data I demoed) to:
- Derive ego-centric networks based on a complete network
- Conduct basic analysis of ego-centric networks
8.3.1 Track R
igraph doc is where you can get started. Play with different parameters to see how results could be different.
Additionally, explore ways to extract ego networks from the complete network using the
Note: If you haven’t done so yet, please check out the video I made in Week 7. You can add new code dealing with ego-centric networks in to your earlier code. (This is when you’re starting to love R if the past few weeks were a bit rough :).)
8.3.2 Track Gephi
The half-minute video below will give you a sense about steps involved in deriving ego-centric networks from a complete network:
Additionally, there are a number of posts that provide more detailed guidance:
- gephi: Centring a graph around an individual node
- My Facebook Network, Part III: Ego Filters and Simple Network Stats
- Page 3 of Introduction to Network Analysis: Working with Gephi (pdf)
Optional if you’re using Windows:
- UCINET provides good support for ego-centric network analysis. You can find a detailed tutorial here.
- E-Net is an SNA tool specially designed for ego network analysis. Here is An Introduction to Personal Network Analysis and Tie Churn Statistics using E-NET. You can also find public datasets from its website.