UC Riverside

The increasing availability of protein-protein interaction graphs (PPI) requires new efficient tools capable of extracting valuable biological knowledge from these networks. Among the wide range of clustering algorithms, Girvan and Newman's edge betweenness algorithm showed remarkable performances in discovering clustering structures in several real-world networks. Unfortunately, their algorithm suffers from high computational cost and it is impractical for inputs of the size of large PPI networks. Here we report on a novel parallel implementation of Girvan and Newman's clustering algorithm that achieves almost linear speed-up for up to 32 processors. The tool is available in the public domain from the authors' website.