Behavioral clusters in dynamic graphs


This paper contributes a method for combining sparse parallel graph algorithms with dense parallel linear algebra algorithms in order to understand dynamic graphs including the temporal behavior of vertices. Our method is the first to cluster vertices in a dynamic graph based on arbitrary temporal behaviors. In order to successfully implement this method, we develop a feature based pipeline for dynamic graphs and apply Nonnegative Matrix Factorization (NMF) to these features. We demonstrate these steps with a sample of the Twitter mentions graph as well as a CAIDA network traffic graph. We contribute and analyze a parallel NMF algorithm presenting both theoretical and empirical studies of performance. This work can be leveraged by graph/network analysts to understand the temporal behavior cluster structure and segmentation structure of dynamic graphs.

Parallel Computing