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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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A bag-of-paths framework for network data analysis.

Kevin Françoisse1, Ilkka Kivimäki2, Amin Mantrach3

  • 1Université catholique de Louvain, Belgium.

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|May 2, 2017
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Summary
This summary is machine-generated.

This study introduces the bag-of-paths (BoP) framework for network analysis, enabling efficient calculation of node relatedness via path probabilities. BoP offers new distance metrics competitive with existing methods for tasks like semi-supervised classification.

Keywords:
Commute-time distanceDistance and similarity on a graphLink analysisNetwork scienceResistance distanceSemi-supervised classification

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Area of Science:

  • Network Science
  • Data Analysis
  • Graph Theory

Background:

  • Analyzing link and network data is crucial for understanding complex systems.
  • Existing methods for network analysis may not fully capture the nuanced relationships between nodes.
  • The concept of path-based relatedness offers a promising avenue for network exploration.

Purpose of the Study:

  • To develop a generic framework, the bag-of-paths (BoP), for comprehensive link and network data analysis.
  • To introduce a novel method for quantifying node relatedness based on probability distributions over network paths.
  • To derive new distance metrics from the BoP framework and evaluate their performance.

Main Methods:

  • Assigning a Gibbs-Boltzmann probability distribution over all paths in a network.
  • Computing path probabilities between nodes using matrix inversion.
  • Deriving two families of node distance measures from BoP probabilities.
  • Extending the Bellman-Ford algorithm using a soft minimum operator to incorporate sub-optimal paths.

Main Results:

  • The BoP framework allows for efficient, closed-form computation of path probabilities between nodes.
  • The derived distance metrics effectively capture node relatedness and accessibility.
  • One distance family interpolates between shortest-path and commute-cost distances.
  • Experimental results show competitive performance of the new distances in semi-supervised classification tasks.

Conclusions:

  • The bag-of-paths framework provides a powerful and flexible tool for network data analysis.
  • BoP enables the computation of novel distance measures that enhance understanding of network topology and node relationships.
  • The framework facilitates the integration of suboptimal paths, offering a more comprehensive view of network connectivity.