Publication Type
Journal Article
Version
publishedVersion
Publication Date
7-2022
Abstract
Network robustness measures how well network structure is strong and healthy when it is under attack, such as vertices joining and leaving. It has been widely used in many applications, such as information diffusion, disease transmission, and network security. However, existing metrics, including node connectivity, edge connectivity, and graph expansion, can be suboptimal for measuring network robustness since they are inefficient to be computed and cannot directly apply to the weighted networks or disconnected networks. In this paper, we define the RR-energy as a new robustness measurement for weighted networks based on the method of spectral analysis. RR-energy can cope with disconnected networks and is efficient to compute with a time complexity of O(|V|+|E|)O(|V|+|E|), where V and E are sets of vertices and edges in the network, respectively. Our experiments illustrate the rationality and efficiency of computing RR-energy: (1) Removal of high degree vertices reduces network robustness more than that of random or small degree vertices; (2) it takes as little as 120 s to compute for a network with about 6M vertices and 33M edges. We can further detect events occurring in a dynamic Twitter network with about 130K users and discover interesting weekly tweeting trends by tracking changes to RR-energy.
Keywords
2-step commute probability, Normalized Laplacian matrix, R-energy, Weighted network
Discipline
Databases and Information Systems | Information Security
Research Areas
Data Science and Engineering
Publication
Knowledge and Information Systems
Volume
64
Issue
7
First Page
1967
Last Page
1996
ISSN
0219-1377
Identifier
10.1007/s10115-022-01670-z
Publisher
Springer Verlag (Germany)
Citation
ZHENG, Jianbing; GAO, Ming; LIM, Ee-peng; LO, David; JIN, Cheqing; and ZHOU, Aoying.
On measuring network robustness for weighted networks. (2022). Knowledge and Information Systems. 64, (7), 1967-1996.
Available at: https://ink.library.smu.edu.sg/sis_research/7233
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/s10115-022-01670-z