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This study introduces a Partition-Aware Connected Components (PACC) algorithm for faster graph analysis. PACC significantly improves the efficiency of finding connected components in large graphs compared to existing methods.

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Area of Science:

  • Computer Science
  • Graph Theory
  • Distributed Systems

Background:

  • Connected components are fundamental in graph analysis for tasks like partitioning and pattern recognition.
  • Existing distributed algorithms for connected components suffer from scalability issues due to high data I/O, intermediate data, and load balancing problems.
  • Efficient computation of connected components is crucial for analyzing large-scale graph data.

Purpose of the Study:

  • To propose a novel, fast, and scalable distributed algorithm for computing connected components.
  • To address the limitations of existing algorithms, specifically concerning data I/O, intermediate data volume, and computational rounds.
  • To improve the efficiency and scalability of connected component analysis in enormous graphs.

Main Methods:

  • Developed a Partition-Aware Connected Components (PACC) algorithm.
  • Employed a two-step processing approach combining partitioning and computation.
  • Utilized edge filtering and graph sketching techniques to reduce data size and computational overhead.
  • Designed PACC to mitigate load balancing issues inherent in distributed graph processing.

Main Results:

  • PACC significantly reduces the size of intermediate data and the input graph.
  • The algorithm minimizes the number of computation rounds required.
  • Achieved 2.9 to 10.7 times performance improvement over state-of-the-art MapReduce and Spark algorithms on real-world graphs.
  • Demonstrated effective load balancing without compromising speed.

Conclusions:

  • PACC offers a substantial advancement in distributed connected component computation.
  • The proposed techniques lead to a more efficient and scalable solution for large-scale graph analysis.
  • PACC provides a practical and high-performance alternative to existing distributed graph algorithms.