Engineering Parallel Algorithms for Community Detection in Massive Networks

TL;DR

This paper develops and evaluates parallel algorithms for community detection in large-scale networks, significantly improving processing speed and scalability for massive graph data.

Contribution

It introduces a flexible framework with novel parallel heuristics, including a parallel Louvain method and ensemble schemes, optimized for massive graph datasets.

Findings

Processing rate often reaches 50 million edges per second

Parallel Louvain and its refinement variant are both fast and effective

Algorithms are suitable for networks with billions of edges

Abstract

The amount of graph-structured data has recently experienced an enormous growth in many applications. To transform such data into useful information, fast analytics algorithms and software tools are necessary. One common graph analytics kernel is disjoint community detection (or graph clustering). Despite extensive research on heuristic solvers for this task, only few parallel codes exist, although parallelism will be necessary to scale to the data volume of real-world applications. We address the deficit in computing capability by a flexible and extensible community detection framework with shared-memory parallelism. Within this framework we design and implement efficient parallel community detection heuristics: A parallel label propagation scheme; the first large-scale parallelization of the well-known Louvain method, as well as an extension of the method adding refinement; and an ensemble scheme combining the above. In extensive experiments driven by the algorithm engineering paradigm, we identify the most successful parameters and combinations of these algorithms. We also compare our implementations with state-of-the-art competitors. The processing rate of our fastest algorithm often reaches 50M edges/second. We recommend the parallel Louvain method and our variant with refinement as both qualitatively strong and fast. Our methods are suitable for massive data sets with billions of edges.