Discovering Important Nodes of Complex Networks Based on Laplacian Spectra

Ali Moradi Amani*, Miquel A. Fiol, Mahdi Jalili, Guanrong Chen, Xinghuo Yu, Lewi Stone

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Knowledge of the Laplacian eigenvalues of a network provides important insights into its structural features and dynamical behaviours. Node or link removal caused by possible outage events, such as mechanical and electrical failures or malicious attacks, significantly impacts the Laplacian spectra. This can also happen due to intentional node removal against which, increasing the algebraic connectivity is desired. In this article, an analytical metric is proposed to measure the effect of node removal on the Laplacian eigenvalues of the network. The metric is formulated based on the local multiplicity of each eigenvalue at each node, so that the effect of node removal on any particular eigenvalues can be approximated using only one single eigen-decomposition of the Laplacian matrix. The metric is applicable to undirected networks as well as strongly-connected directed ones. It also provides a reliable approximation for the 'Laplacian energy' of a network. The performance of the metric is evaluated for several synthetic networks and also the American Western States power grid. Results show that this metric has a nearly perfect precision in correctly predicting the most central nodes, and significantly outperforms other comparable heuristic methods.

Original languageEnglish
Pages (from-to)4146-4158
Number of pages13
JournalIEEE Transactions on Circuits and Systems I: Regular Papers
Issue number10
StatePublished - 1 Oct 2023
Externally publishedYes


  • Complex network
  • Laplacian spectrum
  • graph theory
  • local multiplicity
  • node-removal attack


Dive into the research topics of 'Discovering Important Nodes of Complex Networks Based on Laplacian Spectra'. Together they form a unique fingerprint.

Cite this