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Design and Implementation of a New Local Alignment Algorithm for Multilayer Networks.

Marianna Milano1, Pietro Hiram Guzzi1, Mario Cannataro1

  • 1Department of Medical and Surgical Sciences, Data Analytics Research Center, University Magna Græcia, 88100 Catanzaro, Italy.

Entropy (Basel, Switzerland)
|September 23, 2022
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Summary
This summary is machine-generated.

This study introduces MultiLoAl, a new algorithm for local network alignment (LNA) in multilayer networks. It effectively addresses limitations of existing methods by considering inter-layer edges for improved network comparison.

Keywords:
local network alignmentmultilayer networknetwork alignment

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Area of Science:

  • Computer Science
  • Network Science
  • Data Mining

Background:

  • Network alignment (NA) is crucial for comparing complex networks across various domains.
  • Existing local network alignment (LNA) algorithms struggle with multilayer networks due to their inability to account for inter-layer edges.
  • Multilayer networks are increasingly prevalent in modeling real-world systems, necessitating advanced alignment techniques.

Purpose of the Study:

  • To propose a novel algorithm, MultiLoAl, for local network alignment specifically designed for multilayer networks.
  • To address the limitations of current LNA methods in handling the complexities of inter-layer connections in multilayer networks.
  • To define and develop a heuristic approach for solving the local alignment problem in multilayer network contexts.

Main Methods:

  • Developed the MultiLoAl algorithm, a novel approach for local alignment of multilayer networks.
  • Defined the problem of local alignment within the specific context of multilayer network structures.
  • Proposed a heuristic method to efficiently solve the defined local alignment problem.
  • Implemented a synthetic multilayer network generator for creating evaluation datasets.

Main Results:

  • The MultiLoAl algorithm demonstrates effective performance in local network alignment tasks on multilayer networks.
  • The proposed heuristic provides a viable solution for the complexities introduced by inter-layer edges.
  • Extensive assessments confirm the algorithm's strengths and capabilities in network comparison.
  • The synthetic data generator facilitated robust evaluation and validation of the MultiLoAl algorithm.

Conclusions:

  • MultiLoAl offers a significant advancement for local network alignment in multilayer networks.
  • The algorithm successfully incorporates inter-layer edge information, overcoming limitations of prior methods.
  • This work provides a valuable tool for analyzing complex, interconnected systems represented by multilayer networks.