Network robustness and residual closeness

2018 ◽  
Vol 52 (3) ◽  
pp. 839-847
Author(s):  
Aysun Aytaç ◽  
Zeynep Nihan Odabaş Berberler
Keyword(s):  
Complex Networks ◽  
Interconnection Networks ◽  
Network Reliability ◽  
Central Issue ◽  
Network Robustness ◽  
Graph Theoretic ◽  
Theoretic Concept ◽  
Individual Nodes

A central issue in the analysis of complex networks is the assessment of their robustness and vulnerability. A variety of measures have been proposed in the literature to quantify the robustness of networks and a number of graph-theoretic parameters have been used to derive formulas for calculating network reliability. In this paper, we study the vulnerability of interconnection networks to the failure of individual nodes, using a graph-theoretic concept of residual closeness as a measure of network robustness which provides a much fuller characterization of the network.

Vulnerability Measures of Transformation Graph Gxy+

2015 ◽  
Vol 26 (06) ◽  
pp. 667-675 ◽  
Author(s):  
Aysun Aytaç ◽  
Tufan Turaci
Keyword(s):  
Communication Networks ◽  
Network Topology ◽  
Interconnection Networks ◽  
Network Reliability ◽  
Network Robustness ◽  
Graph Theoretic ◽  
Theoretic Concept ◽  
System Robustness ◽  
The Cost ◽  
Cost Of Construction

Several factors have to be taken into account in the design of large interconnection networks. Optimal design is important both to achieve good performance and to reduce the cost of construction and maintenance. Practical communication networks are exposed to failures of network components. Failures between nodes and connections happen and it is desirable that a network is robust in the sense that a limited number of failures does not break down the whole system. Robustness of the network topology is a key aspect in the design of computer networks. A variety of measures have been proposed in the literature to quantify the robustness of networks and a number of graph-theoretic parameters have been used to derive formulas for calculating network reliability. In this paper, we study the vulnerability of interconnection networks to the failure of individual nodes, using a graph-theoretic concept of domination and strong-weak domination numbers of the transformation graph Gxy+ as a measure of network robustness.

Robustness of Regular Caterpillars

2017 ◽  
Vol 28 (07) ◽  
pp. 835-841 ◽  
Author(s):  
Aysun Aytaç ◽  
Zeynep Nihan Odabaş Berberler
Keyword(s):  
Computer Networks ◽  
Network Topology ◽  
Network Robustness ◽  
Graph Theoretic ◽  
Theoretic Concept

Robustness of the network topology is a key aspect in the design of computer networks. Vertex residual closeness is a new graph-theoretic concept defined as a measure of network robustness. In this model, edges are perfectly reliable and the vertices fail independently of each other. In this paper, vertex residual closeness of paths and regular caterpillars are calculated by giving an insight of how to evaluate the vertex residual closeness of path-like graphs.

Extremal Results on Vertex and Link Residual Closeness

2021 ◽  
pp. 1-21
Author(s):  
Bo Zhou ◽  
Zhenan Li ◽  
Haiyan Guo
Keyword(s):  
Computer Networks ◽  
Network Topology ◽  
Network Robustness ◽  
Unicyclic Graphs ◽  
Graph Theoretic ◽  
Theoretic Concept

Robustness of the network topology is a key aspect in the design of computer networks. Vertex (Link, respectively) residual closeness is a new graph-theoretic concept defined as a measure of network robustness due to the failure of individual vertices (links, respectively). In this paper, we identify the trees and unicyclic graphs with the first a few smallest vertex residual closeness, and determine the graphs that minimize or maximize the vertex (link, respectively) residual closeness over some classes of graphs.

scholarly journals Network robustness improvement via long-range links

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Vincenza Carchiolo ◽  
Marco Grassia ◽  
Alessandro Longheu ◽  
Michele Malgeri ◽  
Giuseppe Mangioni
Keyword(s):  
Complex Networks ◽  
Long Range ◽  
Real World ◽  
Network Robustness ◽  
Scale Free ◽  
Area Of Interest ◽  
Before And After ◽  
Significant Area ◽  
Better Than

AbstractMany systems are today modelled as complex networks, since this representation has been proven being an effective approach for understanding and controlling many real-world phenomena. A significant area of interest and research is that of networks robustness, which aims to explore to what extent a network keeps working when failures occur in its structure and how disruptions can be avoided. In this paper, we introduce the idea of exploiting long-range links to improve the robustness of Scale-Free (SF) networks. Several experiments are carried out by attacking the networks before and after the addition of links between the farthest nodes, and the results show that this approach effectively improves the SF network correct functionalities better than other commonly used strategies.

Exploring wall shear stress spatiotemporal heterogeneity in coronary arteries combining correlation-based analysis and complex networks with computational hemodynamics

2020 ◽  
Vol 234 (11) ◽  
pp. 1209-1222 ◽  
Author(s):  
Karol Calò ◽  
Giuseppe De Nisco ◽  
Diego Gallo ◽  
Claudio Chiastra ◽  
Ayla Hoogendoorn ◽  
...  
Keyword(s):  
Shear Stress ◽  
Wall Shear Stress ◽  
Complex Networks ◽  
Coronary Arteries ◽  
Early Stage ◽  
Flow Direction ◽  
Computational Hemodynamics ◽  
Time Histories ◽  
Wall Shear

Atherosclerosis at the early stage in coronary arteries has been associated with low cycle-average wall shear stress magnitude. However, parallel to the identification of an established active role for low wall shear stress in the onset/progression of the atherosclerotic disease, a weak association between lesions localization and low/oscillatory wall shear stress has been observed. In the attempt to fully identify the wall shear stress phenotype triggering early atherosclerosis in coronary arteries, this exploratory study aims at enriching the characterization of wall shear stress emerging features combining correlation-based analysis and complex networks theory with computational hemodynamics. The final goal is the characterization of the spatiotemporal and topological heterogeneity of wall shear stress waveforms along the cardiac cycle. In detail, here time-histories of wall shear stress magnitude and wall shear stress projection along the main flow direction and orthogonal to it (a measure of wall shear stress multidirectionality) are analyzed in a representative dataset of 10 left anterior descending pig coronary artery computational hemodynamics models. Among the main findings, we report that the proposed analysis quantitatively demonstrates that the model-specific inlet flow-rate shapes wall shear stress time-histories. Moreover, it emerges that a combined effect of low wall shear stress magnitude and of the shape of the wall shear stress–based descriptors time-histories could trigger atherosclerosis at its earliest stage. The findings of this work suggest for new experiments to provide a clearer determination of the wall shear stress phenotype which is at the basis of the so-called arterial hemodynamic risk hypothesis in coronary arteries.

scholarly journals Efficient randomization of biological networks while preserving functional characterization of individual nodes

2016 ◽  
Vol 17 (1) ◽  
Author(s):  
Francesco Iorio ◽  
Marti Bernardo-Faura ◽  
Andrea Gobbi ◽  
Thomas Cokelaer ◽  
Giuseppe Jurman ◽  
...  
Keyword(s):  
Biological Networks ◽  
Functional Characterization ◽  
Individual Nodes

scholarly journals Exploring triad-rich substructures by graph-theoretic characterizations in complex networks

2017 ◽  
Vol 468 ◽  
pp. 53-69 ◽  
Author(s):  
Songwei Jia ◽  
Lin Gao ◽  
Yong Gao ◽  
James Nastos ◽  
Xiao Wen ◽  
...  
Keyword(s):  
Complex Networks ◽  
Graph Theoretic
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