Improving robustness of complex networks via the effective graph resistance

Wang, Xiangrong; Pournaras, Evangelos; Kooij, Robert E.; Mieghem, Piet Van

doi:10.1140/epjb/e2014-50276-0

Cited by 77 publications

(79 citation statements)

References 31 publications

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“…Several graph spectral metrics have been introduced to measure graph robustness against node or link removals. Such metrics are algebraic connectivity [7], spectral gap [8], natural connectivity [9], weighted spectrum [10], network criticality [11], and effective graph resistance [12]. Moreover, there have been several studies to compare a subset of these metrics [9], [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive comparison and accuracy of graph metrics in predicting network resilience

Alenazi

Sterbenz

2015

2015 11th International Conference on the Design of Reliable Communication Networks (DRCN)

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Graph robustness metrics have been used largely to study the behavior of communication networks in the presence of targeted attacks and random failures. Several researchers have proposed new graph metrics to better predict network resilience and survivability against such attacks. Most of these metrics have been compared to a few established graph metrics for evaluating the effectiveness of measuring network resilience. In this paper, we perform a comprehensive comparison of the most commonly used graph robustness metrics. First, we show how each metric is determined and calculate its values for baseline graphs. Using several types of random graphs, we study the accuracy of each robustness metric in predicting network resilience against centrality-based attacks. The results show three conclusions. First, our path diversity metric has the highest accuracy in predicting network resilience for structured baseline graphs. Second, the variance of node-betweenness centrality has mostly the best accuracy in predicting network resilience for Waxman random graphs. Third, path diversity, network criticality, and effective graph resistance have high accuracy in measuring network resilience for Gabriel graphs.

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Section: Introductionmentioning

confidence: 99%

Comprehensive comparison and accuracy of graph metrics in predicting network resilience

Alenazi

Sterbenz

2015

2015 11th International Conference on the Design of Reliable Communication Networks (DRCN)

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“…Combining the definition (10) of the effective graph resistance R G with that of in (7) and the spectral decomposition (11) shows that…”

Section: Background a Electrical Voltage-current Equations In Nementioning

confidence: 99%

“…The effect of the removal of links on R G is analyzed in Ref. [11], and several bounds on R G are deduced. A new, tighter lower bound (B12) for R G is derived in Appendix B.…”

Section: Background a Electrical Voltage-current Equations In Nementioning

confidence: 99%

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Pseudoinverse of the Laplacian and best spreader node in a network

2017

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Determining a set of "important" nodes in a network constitutes a basic endeavor in network science. Inspired by electrical flows in a resistor network, we propose the best conducting node j in a graph G as the minimizer of the diagonal element Q † jj of the pseudoinverse matrix Q † of the weighted Laplacian matrix of the graph G. We propose a new graph metric that complements the effective graph resistance R G and that specifies the heterogeneity of the nodal spreading capacity in a graph. Various formulas and bounds for the diagonal element Q † jj are presented. Finally, we compute the pseudoinverse matrix of the Laplacian of star, path, and cycle graphs and derive an expansion and lower bound of the effective graph resistance R G based on the complement of the graph G.

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“…This is especially the case for social interactions in domains such as healthcare [1], conflicts [2], disease spread [3] and other [4]. Mobile phones, environmental sensors, and social networks can track human mobility, relationships and their significance, especially when the interaction dynamics are represented with temporal networks that model the evolution of interactions [5].…”

Section: Introductionmentioning

confidence: 99%

Mining social interactions in privacy-preserving temporal networks

Musciotto¹,

Delpriori²,

Castagno³

et al. 2016

2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

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Abstract-The opportunities to empirically study temporal networks nowadays are immense thanks to Internet of Things technologies along with ubiquitous and pervasive computing that allow a real-time finegrained collection of social network data. This empowers data analytics and data scientists to reason about complex temporal phenomena, such as disease spread, residential energy consumption, political conflicts etc., using systematic methologies from complex networks and graph spectra analysis. However, a misuse of these methods may result in privacyintrusive and discriminatory actions that may threaten citizens' autonomy and put their life under surveillance. This paper studies highly sparse temporal networks that model social interactions such as the physical proximity of participants in conferences. When citizens can self-determine the anonymized proximity data they wish to share via privacy-preserving platforms, temporal networks may turn out to be highly sparse and have low quality. This paper shows that even in this challenging scenario of privacy-by-design, significant information can be mined from temporal networks such as the correlation of events happening during a conference or stable groups interacting over time. The findings of this paper contribute to the introduction of privacy-preserving data analytics in temporal networks and their applications.

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Improving robustness of complex networks via the effective graph resistance

Cited by 77 publications

References 31 publications

Comprehensive comparison and accuracy of graph metrics in predicting network resilience

Comprehensive comparison and accuracy of graph metrics in predicting network resilience

Pseudoinverse of the Laplacian and best spreader node in a network

Mining social interactions in privacy-preserving temporal networks

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