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CASS: A distributed network clustering algorithm based on structure similarity for large-scale network.

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Summary
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This study introduces an efficient network clustering algorithm for analyzing large datasets using Apache Spark. The novel approach optimizes memory usage and execution time, enabling accurate cluster identification across diverse network types.

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Area of Science:

  • Computer Science
  • Data Science
  • Network Analysis

Background:

  • Analyzing large-scale network data is crucial due to increasing network sizes.
  • Conventional network clustering algorithms face memory limitations in single-machine environments.
  • There is a need for scalable algorithms to handle big network data.

Purpose of the Study:

  • To propose an efficient network clustering algorithm for large-scale network data analysis.
  • To adapt conventional clustering paradigms for the Apache Spark environment.
  • To reduce memory usage and execution time for network clustering.

Main Methods:

  • Developed a novel network clustering algorithm leveraging Apache Spark.
  • Implemented optimization techniques including Bloom filter and shuffle selection.
  • Evaluated algorithm performance using average normalized cut metric.

Main Results:

  • The proposed algorithm successfully analyzed diverse large-scale network datasets (biological, co-authorship, internet topology, social networks).
  • Achieved more accurate clusters with reduced memory usage compared to existing algorithms.
  • Demonstrated scalability and validated biologically meaningful cluster functions.

Conclusions:

  • The Apache Spark-based network clustering algorithm offers an efficient solution for large-scale data.
  • Optimization techniques significantly improve memory efficiency and reduce execution time.
  • The algorithm provides accurate and scalable network clustering with biologically relevant insights.