On the Predictability of Network Robustness from Spectral Measures

Yamashita, K.; Yasuda, Yuichi; Nakamura, Ryô; Ohsaki, Hiroyuki

doi:10.1109/compsac.2019.10178

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…By optimizing a specified spectral measure of the network through random edge-rewiring, the robustness of the resultant network is accordingly enhanced. It is however noticed that, although widely used as an estimator of the robustness for real-world networks, the correlation between spectral measures and the robustness remains unclear [30]. Nevertheless, given a reliable predictive measure or indicator of the network robustness, optimization algorithms can be applied [31], [32]; while if there are more than one predictive measures, multi-objective optimization schemes can be applied instead [33].…”

mentioning

confidence: 99%

Towards Optimal Robustness of Network Controllability: An Empirical Necessary Condition

Lou

Wang

Tsang

et al. 2020

IEEE Trans. Circuits Syst. I

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To better understand the correlation between network topological features and the robustness of network controllability in a general setting, this paper suggests a practical approach to searching for optimal network topologies with given numbers of nodes and edges. Since theoretical analysis is impossible at least in the present time, exhaustive search based on optimization techniques is employed, firstly for a group of small-sized networks that are realistically workable, where exhaustive means 1) all possible network structures with the given numbers of nodes and edges are computed and compared, and 2) all possible node-removal sequences are considered. An empirical necessary condition (ENC) is observed from the results of exhaustive search, which shrinks the search space to quickly find an optimal solution. ENC shows that the maximum and minimum in-and out-degrees of an optimal network structure should be almost identical, or within a very narrow range, i.e., the network should be extremely homogeneous. Edge rectification towards the satisfaction of the ENC is then designed and evaluated. Simulation results on large-sized synthetic and realworld networks verify the effectiveness of both the observed ENC and the edge rectification scheme. As more operations of edge rectification are performed, the network is getting closer to exactly satisfying the ENC, and consequently the robustness of the network controllability is enhanced towards optimum.

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confidence: 99%

Towards Optimal Robustness of Network Controllability: An Empirical Necessary Condition

Lou

Wang

Tsang

et al. 2020

IEEE Trans. Circuits Syst. I

View full text Add to dashboard Cite

show abstract

“…By optimizing a specified spectral measure of the network through random edge-rewiring, the robustness of the resultant network is enhanced consequently. However, it was noted that the correlation between spectral measures and the robustness is indeed unclear [30]. Nevertheless, given a reliable predictive measure or indicator of the network robustness, optimization algorithms can be applied [31]- [34].…”

Section: Introductionmentioning

confidence: 99%

Knowledge-Based Prediction of Network Controllability Robustness

Lou,

He,

Wang

et al. 2020

Preprint

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Network controllability robustness reflects how well a networked system can maintain its controllability against destructive attacks. Its measure is quantified by a sequence of values that record the remaining controllability of the network after a sequence of node-removal or edge-removal attacks. Traditionally, the controllability robustness is studied only for directed networks and is determined by attack simulations, which is computationally time consuming or even infeasible. In the present paper, an improved method for predicting the controllability robustness of undirected networks is developed based on machine learning using a group of convolutional neural networks (CNNs). In this scheme, a number of training data generated by simulations are used to train the group of CNNs for classification and prediction, respectively. Extensive experimental studies are carried out, which demonstrate that 1) the proposed method predicts more precisely than the classical single-CNN predictor; 2) the proposed CNN-based predictor provides a better predictive measure than the traditional spectral measures and network heterogeneity.

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“…A priori measures are easy-to-access and predictive; while a posteriori measures are iteratively calculated after each of the sequence of (simulated) attacks, which are usually time-consuming especially for large-scale networks. However, the predictive a priori measures have limited scopes of applications [36]. Moreover, the a posteriori measures are effective when the attack process is terminated by a specific criterion, whereas the a priori measures do not consider the stopping criteria.…”

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confidence: 99%

Structural Robustness of Complex Networks: A Survey of A Posteriori Measures

Lou,

Wang,

Chen

2023

Preprint

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Network robustness is critical for various industrial and social networks against malicious attacks, which has various meanings in different research contexts and here it refers to the ability of a network to sustain its functionality when a fraction of the network fail to work due to attacks. The rapid development of complex networks research indicates special interest and great concern about the network robustness, which is essential for further analyzing and optimizing network structures towards engineering applications. This comprehensive survey distills the important findings and developments of network robustness research, focusing on the a posteriori structural robustness measures for single-layer static networks. Specifically, the a posteriori robustness measures are reviewed from four perspectives: 1) network functionality, including connectivity, controllability and communication ability, as well as their extensions; 2) malicious attacks, including conventional and computation-based attack strategies; 3) robustness estimation methods using either analytical approximation or machine learning-based prediction; 4) network robustness optimization. Based on the existing measures, a practical threshold of network destruction is introduced, with the suggestion that network robustness should be measured only before reaching the threshold of destruction. Then, a posteriori and a priori measures are compared experimentally, revealing the advantages of the a posteriori measures. Finally, prospective research directions with respect to a posteriori robustness measures are recommended.

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On the Predictability of Network Robustness from Spectral Measures

Cited by 8 publications

References 15 publications

Towards Optimal Robustness of Network Controllability: An Empirical Necessary Condition

Towards Optimal Robustness of Network Controllability: An Empirical Necessary Condition

Knowledge-Based Prediction of Network Controllability Robustness

Structural Robustness of Complex Networks: A Survey of A Posteriori Measures

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