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Title: Generating Massive Scale-free Networks: Novel Parallel Algorithms using the Preferential Attachment Model

Abstract

Recently, there has been substantial interest in the study of various random networks as mathematical models of complex systems. As real-life complex systems grow larger, the ability to generate progressively large random networks becomes all the more important. This motivates the need for efficient parallel algorithms for generating such networks. Naïve parallelization of sequential algorithms for generating random networks is inefficient due to inherent dependencies among the edges and the possibility of creating duplicate (parallel) edges. In this article, we present message passing interface-based distributed memory parallel algorithms for generating random scale-free networks using the preferential-attachment model. Our algorithms are experimentally verified to scale very well to a large number of processing elements (PEs), providing near-linear speedups. The algorithms have been exercised with regard to scale and speed to generate scale-free networks with one trillion edges in 6 minutes using 1,000 PEs.

Authors:
 [1];  [2];  [1];  [3]
+ Show Author Affiliations
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. Texas A & M Univ. at Kingsville, TX (United States)
  3. Univ. of Virginia, Charlottesville, VA (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1631261
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
ACM Transactions on Parallel Computing
Additional Journal Information:
Journal Volume: 7; Journal Issue: 2; Journal ID: ISSN 2329-4949
Publisher:
Association for Computing Machinery
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; network science; random networks; preferential attachment; distributed algorithms

Citation Formats

Alam, Maksudul, Khan, Maleq, Perumalla, Kalyan S., and Marathe, Madhav. Generating Massive Scale-free Networks: Novel Parallel Algorithms using the Preferential Attachment Model. United States: N. p., 2020. Web. doi:10.1145/3391446.
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Alam, Maksudul, Khan, Maleq, Perumalla, Kalyan S., & Marathe, Madhav. Generating Massive Scale-free Networks: Novel Parallel Algorithms using the Preferential Attachment Model. United States. https://doi.org/10.1145/3391446
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Alam, Maksudul, Khan, Maleq, Perumalla, Kalyan S., and Marathe, Madhav. Mon . "Generating Massive Scale-free Networks: Novel Parallel Algorithms using the Preferential Attachment Model". United States. https://doi.org/10.1145/3391446. https://www.osti.gov/servlets/purl/1631261.
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@article{osti_1631261,
title = {Generating Massive Scale-free Networks: Novel Parallel Algorithms using the Preferential Attachment Model},
author = {Alam, Maksudul and Khan, Maleq and Perumalla, Kalyan S. and Marathe, Madhav},
abstractNote = {Recently, there has been substantial interest in the study of various random networks as mathematical models of complex systems. As real-life complex systems grow larger, the ability to generate progressively large random networks becomes all the more important. This motivates the need for efficient parallel algorithms for generating such networks. Naïve parallelization of sequential algorithms for generating random networks is inefficient due to inherent dependencies among the edges and the possibility of creating duplicate (parallel) edges. In this article, we present message passing interface-based distributed memory parallel algorithms for generating random scale-free networks using the preferential-attachment model. Our algorithms are experimentally verified to scale very well to a large number of processing elements (PEs), providing near-linear speedups. The algorithms have been exercised with regard to scale and speed to generate scale-free networks with one trillion edges in 6 minutes using 1,000 PEs.},
doi = {10.1145/3391446},
journal = {ACM Transactions on Parallel Computing},
number = 2,
volume = 7,
place = {United States},
year = {Mon May 18 00:00:00 EDT 2020},
month = {Mon May 18 00:00:00 EDT 2020}
}
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https://doi.org/10.1145/3391446
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