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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 language | English |
|---|---|
| Pages (from-to) | 2757-2764 |
| Number of pages | 8 |
| Journal | IEEE Transactions on Systems, Man, and Cybernetics: Systems |
| Volume | 54 |
| Issue number | 5 |
| Online published | 23 Jan 2024 |
| DOIs | |
| Publication status | Published - 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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Dive into the research topics of 'Introducing a New Edge Centrality Measure: The Connectivity Rank Index'. Together they form a unique fingerprint.Projects
- 1 Finished
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GRF: Analyzing the Robustness of Network Controllability against Malicious Attacks
CHEN, G. (Principal Investigator / Project Coordinator) & TANG, K. S. W. (Co-Investigator)
1/01/21 → 28/05/24
Project: Research
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