A template for parallelizing the louvain method for modularity maximization

Sanjukta Bhowmick, Sriram Srinivasan

Research output: Chapter in Book/Report/Conference proceedingChapter

6 Citations (Scopus)

Abstract

Detecting communities using modularity maximization is an important operation in network analysis. As the size of the networks increase to petascales, it is important to design parallel algorithms to handle the large-scale data. In this chapter, a shared memory (OpenMP-based) implementation of the Louvain method, one of the most popular algorithms for maximizing modularity, is introduced. This chapter also discusses the challenges in parallelizing this algorithm as well as metrics for evaluating the correctness of the results. The results demonstrate that the implementation is highly scalable. Moreover, it also focuses on how this template can be extended to time-varying networks.

Original languageEnglish (US)
Title of host publicationModeling and Simulation in Science, Engineering and Technology
PublisherSpringer Basel
Pages111-124
Number of pages14
DOIs
StatePublished - Jan 1 2013

Publication series

NameModeling and Simulation in Science, Engineering and Technology
Volume55
ISSN (Print)2164-3679
ISSN (Electronic)2164-3725

Fingerprint

Modularity
Template
Time varying networks
OpenMP
Network Analysis
Electric network analysis
Shared Memory
Parallel algorithms
Parallel Algorithms
Correctness
Time-varying
Data storage equipment
Metric
Demonstrate
Community
Design

ASJC Scopus subject areas

  • Modeling and Simulation
  • Engineering(all)
  • Fluid Flow and Transfer Processes
  • Computational Mathematics

Cite this

Bhowmick, S., & Srinivasan, S. (2013). A template for parallelizing the louvain method for modularity maximization. In Modeling and Simulation in Science, Engineering and Technology (pp. 111-124). (Modeling and Simulation in Science, Engineering and Technology; Vol. 55). Springer Basel. https://doi.org/10.1007/978-1-4614-6729-8_6

A template for parallelizing the louvain method for modularity maximization. / Bhowmick, Sanjukta; Srinivasan, Sriram.

Modeling and Simulation in Science, Engineering and Technology. Springer Basel, 2013. p. 111-124 (Modeling and Simulation in Science, Engineering and Technology; Vol. 55).

Research output: Chapter in Book/Report/Conference proceedingChapter

Bhowmick, S & Srinivasan, S 2013, A template for parallelizing the louvain method for modularity maximization. in Modeling and Simulation in Science, Engineering and Technology. Modeling and Simulation in Science, Engineering and Technology, vol. 55, Springer Basel, pp. 111-124. https://doi.org/10.1007/978-1-4614-6729-8_6
Bhowmick S, Srinivasan S. A template for parallelizing the louvain method for modularity maximization. In Modeling and Simulation in Science, Engineering and Technology. Springer Basel. 2013. p. 111-124. (Modeling and Simulation in Science, Engineering and Technology). https://doi.org/10.1007/978-1-4614-6729-8_6
Bhowmick, Sanjukta ; Srinivasan, Sriram. / A template for parallelizing the louvain method for modularity maximization. Modeling and Simulation in Science, Engineering and Technology. Springer Basel, 2013. pp. 111-124 (Modeling and Simulation in Science, Engineering and Technology).
@inbook{e3f7c0bc54d3473a9724b00e0553f1f5,
title = "A template for parallelizing the louvain method for modularity maximization",
abstract = "Detecting communities using modularity maximization is an important operation in network analysis. As the size of the networks increase to petascales, it is important to design parallel algorithms to handle the large-scale data. In this chapter, a shared memory (OpenMP-based) implementation of the Louvain method, one of the most popular algorithms for maximizing modularity, is introduced. This chapter also discusses the challenges in parallelizing this algorithm as well as metrics for evaluating the correctness of the results. The results demonstrate that the implementation is highly scalable. Moreover, it also focuses on how this template can be extended to time-varying networks.",
author = "Sanjukta Bhowmick and Sriram Srinivasan",
year = "2013",
month = "1",
day = "1",
doi = "10.1007/978-1-4614-6729-8_6",
language = "English (US)",
series = "Modeling and Simulation in Science, Engineering and Technology",
publisher = "Springer Basel",
pages = "111--124",
booktitle = "Modeling and Simulation in Science, Engineering and Technology",

}

TY - CHAP

T1 - A template for parallelizing the louvain method for modularity maximization

AU - Bhowmick, Sanjukta

AU - Srinivasan, Sriram

PY - 2013/1/1

Y1 - 2013/1/1

N2 - Detecting communities using modularity maximization is an important operation in network analysis. As the size of the networks increase to petascales, it is important to design parallel algorithms to handle the large-scale data. In this chapter, a shared memory (OpenMP-based) implementation of the Louvain method, one of the most popular algorithms for maximizing modularity, is introduced. This chapter also discusses the challenges in parallelizing this algorithm as well as metrics for evaluating the correctness of the results. The results demonstrate that the implementation is highly scalable. Moreover, it also focuses on how this template can be extended to time-varying networks.

AB - Detecting communities using modularity maximization is an important operation in network analysis. As the size of the networks increase to petascales, it is important to design parallel algorithms to handle the large-scale data. In this chapter, a shared memory (OpenMP-based) implementation of the Louvain method, one of the most popular algorithms for maximizing modularity, is introduced. This chapter also discusses the challenges in parallelizing this algorithm as well as metrics for evaluating the correctness of the results. The results demonstrate that the implementation is highly scalable. Moreover, it also focuses on how this template can be extended to time-varying networks.

UR - http://www.scopus.com/inward/record.url?scp=85014765842&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85014765842&partnerID=8YFLogxK

U2 - 10.1007/978-1-4614-6729-8_6

DO - 10.1007/978-1-4614-6729-8_6

M3 - Chapter

AN - SCOPUS:85014765842

T3 - Modeling and Simulation in Science, Engineering and Technology

SP - 111

EP - 124

BT - Modeling and Simulation in Science, Engineering and Technology

PB - Springer Basel

ER -