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DHPV: a distributed algorithm for large-scale graph partitioning.

Wilfried Yves Hamilton Adoni1, Tarik Nahhal1, Moez Krichen2,3

  • 1LIMSAD Laboratory, Faculty of sciences, Hassan II University of Casablanca, Casablanca, Morocco.

Journal of Big Data
|September 21, 2020
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Summary
This summary is machine-generated.

Graph partitioning is essential for big data analysis. DPHV (Distributed Placement of Hub-Vertices) offers an efficient parallel and distributed heuristic for large-scale graph partitioning, significantly reducing processing time.

Keywords:
Big graphDistributed computingGraph partitioning algorithmsGraphXLarge-scale networksk-Partition

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Area of Science:

  • Computer Science
  • Data Science
  • Graph Theory

Background:

  • Big graphs, central to NoSQL databases, store data in vertices and relationships in edges.
  • Analyzing large graphs is resource-intensive due to millions of vertices and billions of edges.
  • Graph partitioning is a viable alternative to reduce query complexity and improve exploration efficiency.

Purpose of the Study:

  • To introduce DPHV (Distributed Placement of Hub-Vertices), an efficient parallel and distributed heuristic for large-scale graph partitioning.
  • To demonstrate the feasibility and reliability of DPHV on real-world graph data.
  • To evaluate the performance of DPHV against existing graph partitioning methods.

Main Methods:

  • Developed DPHV, a novel heuristic for parallel and distributed graph partitioning.
  • Applied DPHV to real-world graph datasets.
  • Conducted experiments on a 10-node Spark cluster to assess performance.

Main Results:

  • DPHV demonstrated significant time gains in graph partitioning.
  • The proposed method outperformed existing algorithms like JA-BE-JA, Greedy, and DFEP.
  • Experiments confirmed the feasibility and reliability of DPHV for large-scale graph analysis.

Conclusions:

  • DPHV provides an efficient and scalable solution for partitioning big graphs.
  • The heuristic effectively reduces the complexity and cost of graph data exploration.
  • DPHV represents a significant advancement in large-scale graph processing techniques.