Graph partitioning MapReduce-based algorithms for counting triangles in large-scale graphs

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.

Automated summary

This paper proposes two new MapReduce algorithms based on graph partitioning, which solve the problem of duplicate counting triangles in other algorithms. The experimental results show that these two algorithms are highly efficient, especially in very large-scale graphs, outperforming an existing algorithm in terms of execution time performance.