Direction-optimizing label propagation and its application to community detection

Abstract

Label Propagation, while more commonly known as a machine learning algorithm for classification, is also an effective method for detecting communities in networks. We propose a new Direction Optimizing Label Propagation Algorithm (DOLPA) that relies on the use of frontiers and alternates between label push and label pull operations to enhance the performance of the standard Label Propagation Algorithm (LPA). Specifically, DOLPA has parameters for tuning the processing order of vertices in a graph, which in turn reduces the number of edges visited and improves the quality of solution obtained. We apply DOLPA to the community detection problem, present the design and implementation of the algorithm, and discuss its shared-memory parallelization using OpenMP. Empirically, we evaluate our algorithm using synthetic graphs as well as real-world networks. Compared with the state-of-the-art Parallel Label Propagation algorithm, we achieve at least two times the F-Score while reducing the runtime by 50% for synthetic graphs with overlapping communities. We also compare DOLPA against state of the art parallel implementation of the Louvain method using the same graphs and show that DOLPA achieves about three times the F-Score at 10% the runtime.