Orange Blog
By: THOCEVAR, Dec 23, 2017
Speeding Up Network Visualization
The Orange3 Network add-on contains a convenient Network Explorer widget for network visualization. Orange uses an iterative force-directed method (a variation of the Fruchterman-Reingold Algorithm) to layout the nodes on the 2D plane. The goal of force-directed methods is to draw connected nodes close to each other as if the edges that connect the nodes were acting as springs. We also don’t want all nodes crowded in a single point, but would rather have them spaced evenly.
By: AJDA, Jul 18, 2016
Network Analysis with Orange
Visualizing relations between data instances can tell us a lot about our data. Let’s see how this works in Orange. We have a data set on machine learning and data mining conferences and journals, with the number of shared authors for each publication venue reported. We can estimate similarity between two conferences using the author profile of a conference: two conference would be similar if they attract the same authors. The data set is already 9 years old, but obviously, it’s about the principle.
By: BIOLAB, Jun 3, 2013
Network Add-on Published in JSS
NetExplorer, a widget for network exploration, was in orange for over 5 years. Several network analysis widgets were added to Orange since, and we decided to move the entire network functionality to an Orange Network add-on. We recently published a paper Interactive Network Exploration with Orange in the Journal of Statistical Software. We invite you to read the tutorial on network exploration. It is aimed for beginners in this topic, and includes detailed explanation with images.
By: BIOLAB, Jul 29, 2011
NetworkX in Orange
NetworkX – a popular open-source python library for network analysis has finally found its way into Orange. It is now used as a base class for network representation in all Orange modules and widgets. By that, we offered to the widespread network community a fruitful and fun way to visualize and explore networks, using their existing NetworkX scripts. It has never been easier to combine network analysis and visualization with existing machine learning and data discovery methods.