Capítols de llibre
http://hdl.handle.net/2117/5840
Sun, 23 Feb 2020 05:50:42 GMT2020-02-23T05:50:42ZMarshall-Olkin extended Zipf distribution
http://hdl.handle.net/2117/105912
Marshall-Olkin extended Zipf distribution
Pérez Casany, Marta; Duarte López, Ariel; Prat Pérez, Arnau
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.
Wed, 28 Jun 2017 07:17:33 GMThttp://hdl.handle.net/2117/1059122017-06-28T07:17:33ZPérez Casany, MartaDuarte López, ArielPrat Pérez, ArnauBeing 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.Using the Marshall-Olkin extended Zipf distribution in graph generation
http://hdl.handle.net/2117/105744
Using the Marshall-Olkin extended Zipf distribution in graph generation
Duarte López, Ariel; Prat Pérez, Arnau; Pérez Casany, Marta
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.
Fri, 23 Jun 2017 06:39:52 GMThttp://hdl.handle.net/2117/1057442017-06-23T06:39:52ZDuarte López, ArielPrat Pérez, ArnauPérez Casany, MartaBeing 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.Introduction to graph databases
http://hdl.handle.net/2117/28232
Introduction to graph databases
Larriba Pey, Josep; Martínez Bazán, Norbert; Domínguez Sal, David
The use of graphs in analytic environments is getting more and more widespread, with applications in many different environments like social network analysis, fraud detection, industrial management, knowledge analysis, etc. Graph databases are one important solution to consider in the management of large datasets. The course will be oriented to tackle four important aspects of graph management. First, to give a characterization of graphs and the most common operations applied on them. Second, to review the technologies for graph management and focus on the particular case of Sparksee. Third, to analyze in depth some important applications and how graphs are used to solve them. Fourth, to understand the use of benchmarking to make the requirements of the user compatible with the growth of the technologies for graph management.
Tue, 09 Jun 2015 08:37:06 GMThttp://hdl.handle.net/2117/282322015-06-09T08:37:06ZLarriba Pey, JosepMartínez Bazán, NorbertDomínguez Sal, DavidThe use of graphs in analytic environments is getting more and more widespread, with applications in many different environments like social network analysis, fraud detection, industrial management, knowledge analysis, etc. Graph databases are one important solution to consider in the management of large datasets. The course will be oriented to tackle four important aspects of graph management. First, to give a characterization of graphs and the most common operations applied on them. Second, to review the technologies for graph management and focus on the particular case of Sparksee. Third, to analyze in depth some important applications and how graphs are used to solve them. Fourth, to understand the use of benchmarking to make the requirements of the user compatible with the growth of the technologies for graph management.