Add Weighted Bridging Centrality plugin - #336
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The parent version was pinned to 0.11.1 while master/master-forge target 0.11.3. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
setup() unconditionally forced metric.setNormalized(true) before reading it back into the panel, so the checkbox never reflected the user's previous choice. StatSettings.save() was a no-op that read the metric's values but never wrote them anywhere. Persist the Normalized setting via NbPreferences.forModule(...) per this repo's Preferences convention, and drop the dead StatSettings class along with the unused isDirected/isNormalized fields on this class (never read elsewhere). Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
No logic changes. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Every core Gephi Statistics panel (GraphDistancePanel, ModularityPanel, PageRankPanel, etc.) uses org.jdesktop.swingx.JXHeader for a title + description banner. This panel instead used a plain bold JLabel with no description, which looked inconsistent next to other panels in the Statistics UI. Depend on org.gephi:ui-library-wrapper (the wrapper module that exposes org.jdesktop.swingx.*, per this repo's dependency convention) and rebuild the panel around a JXHeader, sized to give the header enough room. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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New plugin or plugin update?
What is the purpose of this plugin?
This plugin extends Gephi’s Bridging Centrality metric to weighted networks. It calculates betweenness centrality using weighted shortest paths, interpreting edge weights as connection strengths and converting them to effective distances using d=1/w. It then combines weighted betweenness centrality with the standard bridging coefficient to identify nodes that play important bridging roles between different regions of a weighted network.
How to test your plugin in Gephi?
Build and install the plugin in Gephi 0.11.1.
Create an undirected weighted network with four nodes (A, B, C, D) and the following edges: A–B with weight 10, B–D with weight 10, A–C with weight 1, and C–D with weight 1.
In the Statistics panel, run Weighted Bridging Centrality with Normalized unchecked.
The plugin should add three columns to the Data Laboratory: Weighted Betweenness Centrality, Bridging Coefficient, and Weighted Bridging Centrality.
The expected results are: Node A: Weighted Betweenness = 0.5, Bridging Coefficient = 0.5, Weighted Bridging Centrality = 0.25. Node B: Weighted Betweenness = 1.0, Bridging Coefficient = 0.5, Weighted Bridging Centrality = 0.50. Node C: Weighted Betweenness = 0.0, Bridging Coefficient = 0.5, Weighted Bridging Centrality = 0.00. Node D: Weighted Betweenness = 0.5, Bridging Coefficient = 0.5, Weighted Bridging Centrality = 0.25.
Edge weights are interpreted as connection strengths and converted to effective distances using d = 1/w. Thus, in this example, the path A–B–D has a total effective distance of 0.2, while A–C–D has a total effective distance of 2.0.
Checklist before submission
masterbranch to get the latest updates?pom.xmlfile