Power-Law Distributed Graph Generation With MapReduce

被引：3

作者：

Angles, Renzo ^{[1
,2
]}

Lopez-Gallegos, Fernanda ^{[2
]}

Paredes, Rodrigo ^{[1
]}

机构：

[1] Univ Talca, Dept Comp Sci, Fac Engn, Curico 3340000, Chile

[2] Millennium Inst Fdn Res Data, Curico 3340000, Chile

来源：

IEEE ACCESS | 2021年 / 9卷

关键词：

Generators; Tools; Cluster computing; Computational modeling; Directed graphs; Benchmark testing; Scalability; Graph generator; MapReduce; Hadoop; MODEL;

D O I：

10.1109/ACCESS.2021.3093329

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A graph generator is a tool which allows to create graph-like data whose structural properties are very similar to those found in real world networks. This paper presents two methods to generate graphs with power-law edge distribution based on the MapReduce processing model that can be easily implemented to run on top of Apache Hadoop. The proposed methods allow the generation of directed and undirected power-law distributed graphs without repeated edges. Our experimental evaluation shows that our methods are efficient and scalable in terms of both graph size and cluster capacity.

引用

页码：94405 / 94415

页数：11

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