Parallel Modularity Clustering

In this paper we develop a parallel approach for computing the modularity clustering often used to identify and analyse communities in social networks. We show that modularity can be approximated by looking at the largest eigenpairs of the weighted graph adjacency matrix that has been perturbed by a rank one update. Also, we generalize this formulation to identify multiple clusters at once. We develop a fast parallel implementation for it that takes advantage of the Lanczos eigenvalue solver and k-means algorithm on the GPU. Finally, we highlight the performance and quality of our approach versus existing state-of-the-art techniques.

Authors: 
Alexandre Fender (NVIDIA)
Nahid Emad (LI-PaRAD, University of Versailles, France)
Serge Petiton (University of Lille I, Sciences & Technologies, France)
Publication Date: 
Monday, June 12, 2017