Improving model performance of shortest-path-based centrality measures in network models through scale space

Kenan Menguc*, Alper Yilmaz

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The quality of the solution in resolving a complex network depends on either the speed or accuracy of the results. While some health studies prioritize high performance, fast algorithms are favored in scenarios requiring rapid decision-making. A comprehensive understanding of the problem necessitates a detailed analysis of the network and its individual components. Betweenness Centrality (BC) and Closeness Centrality (CC) are commonly employed measures in network studies. This study introduces a new strategy to compute BC and CC that assesses their sensitivity in the scale space while measuring the shortest path. The scale space is generated by incorporating a scale parameter that is shown to achieve up to 60% performance improvements for various datasets. The study provides in-depth insights into the importance of the scale space analysis. Finally, a flexible measurement tool is provided that is suitable for various types of problems. To demonstrate the flexibility and applicability, we experimented with two methods for 10 different graphs using the proposed approach.

Original languageEnglish
Article numbere8082
JournalConcurrency Computation Practice and Experience
Volume36
Issue number14
DOIs
Publication statusPublished - 25 Jun 2024

Bibliographical note

Publisher Copyright:
© 2024 John Wiley & Sons Ltd.

Keywords

  • betweenness centrality
  • closeness centrality
  • feature engineering
  • scale space
  • sensitivity
  • shortest-path
  • transportation network

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