.png){kind=icon}
.png){kind=icon}
Return
Network coherence in the web graphs
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cnsns.2015.03.011.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Network coherence is used to characterize the consensus dynamics with additive stochastic disturbances and can be described by Laplacian spectrum. In this paper, we mainly obtain the scalings of network coherence in the web graphs with a special feature that its fractal dimension is infinite. We then investigate the relationship between the scalings and fractal dimension. Based on the structures of web graphs, we obtain the relationships for Laplacian matrix and Laplacian eigenvalues between web graphs and their corresponding equilateral polygons. We also obtain analytical expressions for the sum of the reciprocals and square reciprocals of all nonzero Laplacian eigenvalues. Finally we calculate first and second order coherence and see that the scalings of network coherence with network size N are N and N-3, which shows that the scalings are not related to the fractal dimension of web graphs. In addition, the scalings of network coherence in web graphs are larger than those performed on some fractal networks. (C) 2015 Elsevier B.V. All rights reserved.
Keywords:
Network coherence
Laplacian spectrum
Web graphs
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.8
Papers:
9.2K
Citations:
1.8W