Degree and Principal Eigenvectors in Complex Networks

  • Cong Li
  • Huijuan Wang
  • Piet Van Mieghem
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7289)


The largest eigenvalue λ 1 of the adjacency matrix powerfully characterizes dynamic processes on networks, such as virus spread and synchronization. The minimization of the spectral radius by removing a set of links (or nodes) has been shown to be an NP-complete problem. So far, the best heuristic strategy is to remove links/nodes based on the principal eigenvector corresponding to the largest eigenvalue λ 1. This motivates us to investigate properties of the principal eigenvector x 1 and its relation with the degree vector. (a) We illustrate and explain why the average E[x 1] decreases with the linear degree correlation coefficient ρ D in a network with a given degree vector; (b) The difference between the principal eigenvector and the scaled degree vector is proved to be the smallest, when \(\lambda _{1}=\frac{N_{2}}{N_{1}}\), where N k is the total number walks in the network with k hops; (c) The correlation between the principal eigenvector and the degree vector decreases when the degree correlation ρ D is decreased.


networks spectral radius principal eigenvector degree assortativity 


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Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • Cong Li
    • 1
  • Huijuan Wang
    • 1
  • Piet Van Mieghem
    • 1
  1. 1.Faculty of Electrical Engineering, Mathematics and Computer ScienceDelft University of TechnologyDelftThe Netherlands

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