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Title: Parallel heuristics for scalable community detection

Community detection has become a fundamental operation in numerous graph-theoretic applications. Despite its potential for application, there is only limited support for community detection on large-scale parallel computers, largely owing to the irregular and inherently sequential nature of the underlying heuristics. In this paper, we present parallelization heuristics for fast community detection using the Louvain method as the serial template. The Louvain method is an iterative heuristic for modularity optimization. Originally developed in 2008, the method has become increasingly popular owing to its ability to detect high modularity community partitions in a fast and memory-efficient manner. However, the method is also inherently sequential, thereby limiting its scalability. Here, we observe certain key properties of this method that present challenges for its parallelization, and consequently propose heuristics that are designed to break the sequential barrier. For evaluation purposes, we implemented our heuristics using OpenMP multithreading, and tested them over real world graphs derived from multiple application domains. Compared to the serial Louvain implementation, our parallel implementation is able to produce community outputs with a higher modularity for most of the inputs tested, in comparable number or fewer iterations, while providing real speedups of up to 16x using 32 threads.
Authors:
 [1] ;  [2] ;  [1]
  1. Washington State University, Pullman, WA (United States). School of Electrical Engineering and Computer Science
  2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States). Fundamental and Computational Sciences Directorate
Publication Date:
Report Number(s):
PNNL-SA-108735
Journal ID: ISSN 0167-8191; KJ0401000
Grant/Contract Number:
AC05-76RL01830
Type:
Published Article
Journal Name:
Parallel Computing
Additional Journal Information:
Journal Volume: 47; Journal Issue: C; Journal ID: ISSN 0167-8191
Research Org:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; community detection; Parallel graph heuristics; Graph coloring; Graph clustering
OSTI Identifier:
1198720
Alternate Identifier(s):
OSTI ID: 1208748