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

Authors:

Kalyan S. Perumalla,

Madhav MaratheAuthors Info & Claims

ACM Transactions on Parallel Computing (TOPC), Volume 7, Issue 2

Article No.: 13, Pages 1 - 35

https://doi.org/10.1145/3391446

Published: 18 May 2020 Publication History

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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.

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Cited By

Li WHu YJiang CWu SBai QLai E(2023)ABEM: An adaptive agent-based evolutionary approach for influence maximization in dynamic social networksApplied Soft Computing10.1016/j.asoc.2023.110062136(110062)Online publication date: Mar-2023
https://doi.org/10.1016/j.asoc.2023.110062
Bissett KCadena JKhan MKuhlman C(2021)Agent-Based Computational Epidemiological ModelingJournal of the Indian Institute of Science10.1007/s41745-021-00260-2101:3(303-327)Online publication date: 5-Oct-2021
https://doi.org/10.1007/s41745-021-00260-2

Index Terms

Generating Massive Scale-free Networks: Novel Parallel Algorithms using the Preferential Attachment Model
1. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory
      1. Graph algorithms
      2. Random graphs
2. Theory of computation
  1. Design and analysis of algorithms
    1. Distributed algorithms
    2. Parallel algorithms
  2. Randomness, geometry and discrete structures
    1. Random network models

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cover image ACM Transactions on Parallel Computing

ACM Transactions on Parallel Computing Volume 7, Issue 2

June 2020

182 pages

ISSN:2329-4949

EISSN:2329-4957

DOI:10.1145/3400890

Editor:
David A. Bader
New Jersey Institute of Technology, USA

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Copyright © 2020 ACM.

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Publication History

Published: 18 May 2020

Online AM: 07 May 2020

Accepted: 01 March 2020

Revised: 01 August 2019

Received: 01 June 2018

Published in TOPC Volume 7, Issue 2

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Cited By

Li WHu YJiang CWu SBai QLai E(2023)ABEM: An adaptive agent-based evolutionary approach for influence maximization in dynamic social networksApplied Soft Computing10.1016/j.asoc.2023.110062136(110062)Online publication date: Mar-2023
https://doi.org/10.1016/j.asoc.2023.110062
Bissett KCadena JKhan MKuhlman C(2021)Agent-Based Computational Epidemiological ModelingJournal of the Indian Institute of Science10.1007/s41745-021-00260-2101:3(303-327)Online publication date: 5-Oct-2021
https://doi.org/10.1007/s41745-021-00260-2

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