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Summary
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This study introduces a new way to find shortest paths in complex, weighted social networks. The novel approach accounts for relationship variety and strength, improving network analysis and identifying influential nodes.

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Area of Science:

  • Network Science
  • Optimization Theory
  • Social Network Analysis

Background:

  • The shortest path problem is fundamental in network optimization.
  • Real-world interactions are complex, heterogeneous, and weighted (multiplex networks).
  • Existing shortest path methods inadequately address multiplex network features.

Purpose of the Study:

  • To introduce a new distance measure for multiplex networks.
  • To account for heterogeneity and weighted relations in shortest path calculations.
  • To model shortest path finding as a multi-objective decision-making problem.

Main Methods:

  • Developed a novel distance measure using Pareto efficiency for multiplex networks.
  • Formulated the shortest path problem as a multi-objective decision-making process.
  • Proposed an exact algorithm to find the set of all shortest paths.

Main Results:

  • Evaluated the method on five real-world datasets, demonstrating the impact of weights and multiplexity.
  • Redefined betweenness centrality using the proposed shortest paths.
  • Applied the redefined centrality to identify influential nodes in a country trade network.

Conclusions:

  • The proposed distance measure effectively captures heterogeneity and weights in multiplex networks.
  • The new approach enhances shortest path analysis and node influence identification.
  • This work provides a more accurate method for analyzing complex real-world interactions.