As a part of my work on cross-community analysis, I needed to compute centrality of whole clusters. Particularly, I was interested in their betweenness centrality as defined by Borgatti & Everett. The computation of this score by classic algorithm by Brandes can be quite expensive in case of many groups in the network, but Puzis et al. proposed a faster alternative, which cleverly precomputes certain data. Unfortunately, a reference implementation of this algorithm in Python does not work with weighted graphs and actually didn’t fit into my analytical toolkit either. Therefore I decided to implement it in Java on top of JUNG. You can find the patch here. It works with JUNG 2.0.1, but likely also with 2.0.0.

# Group Betweenness Centrality in JUNG (Java)

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