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Introducing a New Edge Centrality Measure: The Connectivity Rank Index

  • Jin Zhou
  • , Yanqi Zhang*
  • , Jun-An Lu*
  • , Guanrong Chen
  • *Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

A new edge centrality measure, connectivity rank index (CRI), is proposed based on the effect of an edge on the network algebraic connectivity. Compared with the existing indices, the CRI can determine the importance of a present edge as well as an absent edge. For large-scale networks, the algorithm based on original CRI definition has high-time complexity. Therefore, an approximation algorithm is designed using the eigenvector elements corresponding to the second smallest Laplacian eigenvalue. This algorithm can identify the most influential edges and the least influential ones easily, which reduces the time complexity from the exhaustive searching scheme with O(N5) to O(N3) in a network of size N. Some examples are shown to verify the effectiveness of the algorithm and the theoretical results. © 2013 IEEE.
Original languageEnglish
Pages (from-to)2757-2764
Number of pages8
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume54
Issue number5
Online published23 Jan 2024
DOIs
Publication statusPublished - May 2024

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 62173254, Grant 61773294, and Grant 62176099; and in part by the Hong Kong Research Grants Council under the GRF Grant CityU 11206320.

Research Keywords

  • Algebra connectivity
  • complex network
  • edge centrality
  • eigenvalue
  • eigenvector

RGC Funding Information

  • RGC-funded

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