Parallel Toolkit for Measuring the Quality of Network Community Structure

TL;DR

This paper introduces a parallel toolkit for efficiently measuring the quality of community structures in large networks, addressing computational challenges in community detection and metric calculation.

Contribution

It presents the first parallel algorithms for community quality metrics, significantly improving performance on distributed and shared memory systems.

Findings

Significant reduction in computation time for community metrics

Achieved high speedup and efficiency in parallel implementations

Effective performance on both distributed and shared memory architectures

Abstract

Many networks display community structure which identifies groups of nodes within which connections are denser than between them. Detecting and characterizing such community structure, which is known as community detection, is one of the fundamental issues in the study of network systems. It has received a considerable attention in the last years. Numerous techniques have been developed for both efficient and effective community detection. Among them, the most efficient algorithm is the label propagation algorithm whose computational complexity is O(|E|). Although it is linear in the number of edges, the running time is still too long for very large networks, creating the need for parallel community detection. Also, computing community quality metrics for community structure is computationally expensive both with and without ground truth. However, to date we are not aware of any effort to introduce parallelism for this problem. In this paper, we provide a parallel toolkit to calculate the values of such metrics. We evaluate the parallel algorithms on both distributed memory machine and shared memory machine. The experimental results show that they yield a significant performance gain over sequential execution in terms of total running time, speedup, and efficiency.