Effective graph resistance

Ellens, Wendy; Spieksma, Floske M.; Mieghem, Piet Van; Jamakovic, Almerima; Kooij, Robert E.

doi:10.1016/j.laa.2011.02.024

Cited by 221 publications

(197 citation statements)

References 10 publications

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“…The sum of all inter-node resistance distances is generally defined as the Kirchoff Index of a graph [55], a graph measure believed to be closely related to network robustness [68]. Recent work [37] has found a close relationship between this measure and a power system's vulnerability to cascading failures.…”

Section: ) Thevenin Impedance Distancementioning

confidence: 99%

Visualizing the Electrical Structure of Power Systems

Cuffe

Keane

2017

IEEE Systems Journal

105

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Section: ) Thevenin Impedance Distancementioning

confidence: 99%

Visualizing the Electrical Structure of Power Systems

Cuffe

Keane

2017

IEEE Systems Journal

105

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“…The EGR metric measures the number of paths between two nodes and their length. The smaller the EGR value is, the more robust the network [15].…”

Section: Structural Metricsmentioning

confidence: 99%

Robustness Comparison of 15 Real Telecommunication Networks: Structural and Centrality Measurements

2016

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Multiple failures can have catastrophic consequences on the normal operation of telecommunication networks. In this sense, guaranteeing network robustness to avoid users and services being disconnected is essential. A wide range of metrics have been proposed for measuring network robustness. In this paper the taxonomy of robustness metrics in telecommunication networks has been extended and a classification of multiple failures scenarios has been made. Moreover, a structural and centrality robustness comparison of 15 real telecommunication networks experiencing multiple failures was carried out. Through this analysis the topological properties which are common for grouping networks with similar robustness are able to be identified.

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“…A measure of the correlation of degrees of neighbouring nodes, reflecting how similar the connections are in the graph with respect to the node degree [30].…”

Section: Degree Assortativitymentioning

confidence: 99%

2D Adaptive Grid-Based Image Analysis Approach for Biological Networks

Alhasson

Obara

2016

2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE)

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Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract-The accurate analysis of biological networks, enabled by the precise capture of their individual components, can reveal important underlying biological principles. Efficient image processing techniques are required to precisely identify and quantify the networks from complex images. In this paper, we present a novel approach for a weighted and undirected graph-based network reconstruction and quantification from 2D images using an adaptive rectangular mesh refinement approach. The proposed approach is able to efficiently identify the organizational principles of the network, capturing the underlying network structure, and computing relevant network topological properties. We validate the proposed approach by comparing it with the state-of-the-art method.

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Effective graph resistance

Cited by 221 publications

References 10 publications

Visualizing the Electrical Structure of Power Systems

Visualizing the Electrical Structure of Power Systems

Robustness Comparison of 15 Real Telecommunication Networks: Structural and Centrality Measurements

2D Adaptive Grid-Based Image Analysis Approach for Biological Networks

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