International Journal of Parallel Programming

, Volume 45, Issue 1, pp 109–127 | Cite as

Parallel Algorithms for Generating Random Networks with Given Degree Sequences

  • Maksudul AlamEmail author
  • Maleq Khan


Random networks are widely used for modeling and analyzing complex processes. Many mathematical models have been proposed to capture diverse real-world networks. One of the most important aspects of these models is degree distribution. Chung–Lu (CL) model is a random network model, which can produce networks with any given arbitrary degree distribution. The complex systems we deal with nowadays are growing larger and more diverse than ever. Generating random networks with any given degree distribution consisting of billions of nodes and edges or more has become a necessity, which requires efficient and parallel algorithms. We present an MPI-based distributed memory parallel algorithm for generating massive random networks using CL model, which takes \(O\left( \frac{m+n}{P}+P\right) \) time with high probability and O(n) space per processor, where n, m, and P are the number of nodes, edges and processors, respectively. The time efficiency is achieved by using a novel load-balancing algorithm. Our algorithms scale very well to a large number of processors and can generate massive power–law networks with one billion nodes and 250 billion edges in one minute using 1024 processors.


Massive Networks Parallel Algorithms Network Generator 



This work has been partially supported by DTRA Grant HDTRA1-11-1-0016, DTRA CNIMS Contract HDTRA1-11-D-0016-0001, NSF NetSE Grant CNS-1011769, NSF SDCI Grant OCI-1032677, and NSF DIBBs Grant ACI-1443054.


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of Computer ScienceVirginia Bioinformatics Institute, Virginia TechBlacksburgUSA
  2. 2.Network Dynamics and Simulation Science LaboratoryVirginia Bioinformatics Institute, Virginia TechBlacksburgUSA

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