2017/08/24 by Hasanuzzaman Bhuiyan, Bhuiyan, Hasanuzzaman, Maleq Khan +3
Computer Science · Physics and Astronomy · #Algorithms and Data Compression #Complex Network Analysis Techniques #Graph Theory and Algorithms #cs.DC
paper · pdf · doi:10.48550/arxiv.1708.07290
10 pages
arxiv created 2017/09/11 · arxiv updated 2017/09/12
Random graphs (or networks) have gained a significant increase of interest due to its popularity in modeling and simulating many complex real-world systems. Degree sequence is one of the most important aspects of these systems. Random graphs with a given degree sequence can capture many characteristics like dependent edges and non-binomial degree distribution that are absent in many classical random graph models such as the Erdős-Rényi graph model. In addition, they have important applications in the uniform sampling of random graphs, counting the number of graphs having the same degree sequence, as well as in string theory, random matrix theory, and matching theory. In this paper, we present an OpenMP-based shared-memory parallel algorithm for generating a random graph with a prescribed degree sequence, which achieves a speedup of 20.5 with 32 cores. One of the steps in our parallel algorithm requires checking the Erdős-Gallai characterization, i.e., whether there exists a graph obeying the given degree sequence, in parallel. This paper presents the first non-trivial parallel algorithm for checking the Erdős-Gallai characterization, which achieves a speedup of 23 using 32 cores.