Marshall-Olkin extended Zipf distribution
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 r...
| Autores: | , , |
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| Tipo de documento: | capítulo de livro |
| Data de publicação: | 2015 |
| País: | España |
| Recursos: | Universitat Politècnica de Catalunya (UPC) |
| Repositório: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglês |
| OAI Identifier: | oai:upcommons.upc.edu:2117/105912 |
| Acesso em linha: | https://hdl.handle.net/2117/105912 https://dx.doi.org/10.1007/978-3-319-27308-2_40 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Mathematical statistics zipf distribution node degree network analysis Estadística matemàtica Distribució (Teoria de la probabilitat) Àrees temàtiques de la UPC::Matemàtiques i estadística |
| Resumo: | 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. |
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