Automate NodeXL Pro > Automate your Wikipedia network analysis!
You can easily learn how to automate NodeXL Pro with “Data Recipes” a.k.a. “NodeXL Options Files” which enable users to perform a full social network and content analysis along with a network visualization with just a few clicks. The key is Task Automation.
The following NodeXL Pro “data recipes” are designed to analyze Wikipedia network data with just a few clicks. The files contain best practice settings. You can use these as suggestions and easily customize them to your own specifications and save them for future use.
Wikipedia Page Network 01 – standard
This recipe is designed for Wikipedia Article-Article networks (1.5 degrees) imported with the MediaWiki Page Network importer. All relevant steps to conduct a full-scale social network analysis are performed. Content analysis is run on the Content column containing the first paragraphs of the respective page. The graph shows an image and a label for every page in the network.
Vertex: Wikipedia article page
Edge: link to mentioned articles on page
Vertex size: Betweenness centrality
Group clustering algorithm: Clauset-Newman-Moore
Group labels: Top 10 most frequently used words
Sentiment language: English
Layout algorithm: Harel-Koren Fast Multiscale
Box layout algorithm: Group-in-a-Box, Treemap
Wikipedia Page Network 02 – large
This recipe is designed to visualize large Wikipedia Article-Article networks (2.0) degrees) imported with the MediaWiki Page Network importer. The graph shows an image and a label only for pages with high Indegree.
Vertex size: Indegree
Group clustering algorithm: Clauset-Newman-Moore
Layout algorithm: Harel-Koren Fast Multiscale
Box layout algorithm: Group-in-a-Box, force-directed
Wikipedia User Network 01 – standard
This recipe is designed for Wikipedia User-User networks imported with the MediaWiki Page Network importer. Text analysis is run on the Comment column. The graph shows a disk for every user in the discussion.
Vertex: Wikipedia User
Edge: Comment
Vertex size: Betweenness centrality
Group clustering algorithm: Clauset-Newman-Moore
Group labels: Top 10 most frequently used words
Text analysis: Top words/word pairs
Layout algorithm: Harel-Koren Fast Multiscale
Box layout algorithm: Group-in-a-Box, Treemap