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Temporal network alignment via GoT-WAVE.

David Aparício1, Pedro Ribeiro1, Tijana Milenković2

  • 1CRACS & INESC-TEC, Departamento de Ciência de Computadores, Faculdade de Ciências, Universidade do Porto, Porto, Portugal.

Bioinformatics (Oxford, England)
|February 14, 2019
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Summary
This summary is machine-generated.

GoT-WAVE enhances network alignment (NA) for temporal networks by using graphlet-orbit transitions (GoTs) for node conservation. This new method improves accuracy and speed, and supports directed edges.

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Area of Science:

  • Computational Biology
  • Network Science
  • Data Mining

Background:

  • Network alignment (NA) identifies conserved regions in networks.
  • Node conservation (NC) and edge conservation are key NA optimization goals.
  • Dynamic graphlet degree vectors are a leading NC measure for temporal networks, used in DynaWAVE.

Purpose of the Study:

  • Introduce graphlet-orbit transitions (GoTs) as a novel dynamic NC measure.
  • Integrate GoTs into the DynaWAVE framework to create GoT-WAVE.
  • Evaluate GoT-WAVE's performance for temporal network alignment.

Main Methods:

  • Developed GoT-WAVE by replacing the dynamic NC measure in DynaWAVE with GoTs.
  • Utilized graphlet-orbit transitions (GoTs) to quantify temporal node similarity.
  • Tested GoT-WAVE on synthetic and real-world temporal networks.

Main Results:

  • GoT-WAVE improved accuracy by 30% and speed by 64% on synthetic networks compared to DynaWAVE.
  • GoT-WAVE and DynaWAVE showed complementary performance on real networks when optimizing dynamic NC.
  • GoT-WAVE is the first temporal NA method to support directed edges.

Conclusions:

  • GoT-WAVE is an efficient and accurate temporal network alignment algorithm.
  • GoT-WAVE offers a promising new approach for analyzing temporal networks, especially those with directed edges.
  • Source code and a user interface for GoT-WAVE are publicly available.