Using the Marshall-Olkin Extended Zipf Distribution in Graph Generation
Being able to generate large synthetic graphs resembling those found in the real world, is of high importance for the design of new graph algorithms and benchmarks. In this paper, we first compare several probability models in terms of goodness-of-fit, when used to model the degree distribution of real graphs. Second, after confirming that the MOEZipf model is the one that gives better fits, we present a method to generate MOEZipf distributions. The method is shown to work well in practice when implemented in a scalable synthetic graph generator.
The authors, all members of DAMA-UPC, thank the Ministry of Science and Innovation of Spain, Generalitat de Catalunya, for grant numbers TIN2013-47008-R and SGR2014-890 respectively and also the EU FP7/2007-2013 for funding the LDBC project (ICT2011-8-317548). M. Pérez-Casany also thanks the Spanish Ministry of Education and Science for grant MTM2013-43992-R and Generalitat de Catalunya for grant 2014 SGR 890 (AGAUR). The authors thank Oracle Labs for the strategic support to the Graphalytics project.
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