Parallel triangle counting over large graphs

Abstract

Counting the number of triangles in a graph is significant for complex network analysis. However, with the rapid growth of graph size, the classical centralized algorithms can not process triangle counting efficiently. Though some researches have proposed parallel triangle counting implementations on Hadoop, the performance enhancement remains a challenging task. To efficiently solve the parallel triangle counting problem, we put forward a hybrid parallel triangle counting algorithm with efficient pruning methods. In addition, we propose a parallel sample algorithm which can avoid repeated edge sampling and produce highprecision results. We implement our patterns based on bulk synchronous parallel framework. Compared with the Hadoop-based implementation, 2 to 13 times gains can be obtained in terms of executing time. © Springer-Verlag 2013.