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Area of Science:

  • Computer Science
  • Graph Theory
  • Distributed Computing

Background:

  • Counting triangles is fundamental for graph analytics tasks like clustering coefficient and transitivity ratio.
  • MapReduce is a prevalent framework for large-scale graph analysis on clusters.
  • Existing algorithms often struggle with duplicate triangle counting.

Purpose of the Study:

  • To propose novel MapReduce algorithms for accurate and efficient triangle counting.
  • To address the challenge of duplicate counting in large-scale graph analysis.
  • To enhance the performance of triangle counting in distributed environments.

Main Methods:

  • Developed two new MapReduce algorithms leveraging graph partitioning techniques.
  • Designed algorithms to prevent the issue of counting the same triangle multiple times.
  • Implemented and tested algorithms on large-scale graph datasets.

Main Results:

  • The proposed algorithms demonstrate high efficiency in counting graph triangles.
  • Achieved superior execution time performance compared to an existing algorithm.
  • Effectiveness particularly pronounced on very large-scale graphs.

Conclusions:

  • The novel MapReduce algorithms offer an efficient solution for triangle counting.
  • Graph partitioning is an effective strategy for optimizing distributed graph analytics.
  • These algorithms provide a significant improvement for large-scale graph analysis tasks.