Abstract

Counting number of triangles in the graph is considered a major task in many large-scale graph analytics problems such as clustering coefficient, transitivity ratio, trusses, etc. In recent years, MapReduce becomes one of the most popular and powerful frameworks for analyzing large-scale graphs in clusters of machines. In this paper, we propose two new MapReduce algorithms based on graph partitioning. The two algorithms avoid the problem of duplicate counting triangles that other algorithms suffer from. The experimental results show a high efficiency of the two algorithms in comparison with an existing algorithm, overcoming it in the execution time performance, especially in very large-scale graphs.

Details

Title
Graph partitioning MapReduce-based algorithms for counting triangles in large-scale graphs
Author
Sharafeldeen, Ahmed 1 ; Alrahmawy, Mohammed 1 ; Elmougy, Samir 1 

 Mansoura University, Department of Computer Science, Faculty of Computers and Information, Mansoura, Egypt (GRID:grid.10251.37) (ISNI:0000000103426662) 
Pages
166
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2760730553
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.