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Power-Law Distributed Graph Generation With MapReduce
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DOI：10.1109/ACCESS.2021.3093329.png)
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Abstract
En 中文
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.
Keywords:
Generators
Tools
Cluster computing
Computational modeling
Directed graphs
Benchmark testing
Scalability
Graph generator
MapReduce
Hadoop
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