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LazyFox: fast and parallelized overlapping community detection in large graphs.

Tim Garrels1,2, Athar Khodabakhsh1, Bernhard Y Renard1,2,3

  • 1Hasso Plattner Institute for Digital Engineering gGmbH, Potsdam, Germany.

Peerj. Computer Science
|June 22, 2023
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Summary
This summary is machine-generated.

LazyFox efficiently detects overlapping communities in large graph datasets. This multi-threaded adaptation of the Fox algorithm speeds up analysis, enabling insights from complex networks previously unfeasible.

Keywords:
C++ toolCommunity analysisGraph algorithmHeuristic triangle estimationLarge networksOpen sourceOverlapping community detectionParallelized algorithmRuntime improvementWeighted clustering coefficient

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Area of Science:

  • Computer Science
  • Data Science
  • Network Analysis

Background:

  • Community detection in graphs is crucial for understanding structure in diverse fields.
  • Existing methods often yield non-overlapping communities, limiting analysis of complex real-world data.
  • Overlapping community detection is computationally expensive with current algorithms.

Purpose of the Study:

  • To develop a faster algorithm for detecting overlapping communities in large graph datasets.
  • To improve upon the efficiency of the Fox algorithm for community detection.
  • To enable the analysis of significantly larger and more complex graph structures.

Main Methods:

  • The study adapts the Fox algorithm, which measures node-community closeness via triangle approximation.
  • A multi-threaded approach, LazyFox, was developed to parallelize the computation.
  • The algorithm's performance was evaluated on large-scale graph datasets.

Main Results:

  • LazyFox significantly reduces computation time for overlapping community detection.
  • The algorithm achieves faster detection without compromising community quality.
  • Enables analysis of graphs with millions of nodes and billions of edges in days, not weeks.

Conclusions:

  • LazyFox offers a scalable and efficient solution for overlapping community detection.
  • The method facilitates the analysis of complex networks previously intractable.
  • The implementation is publicly available as an open-source tool.