Optimization of network robustness to waves of targeted and random attacks

Tanizawa, Toshihiro; Paul, Gerald; Cohen, Reuven; Havlin, Shlomo; Stanley, H. Eugene

doi:10.1103/physreve.71.047101

Cited by 181 publications

(133 citation statements)

References 19 publications

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“…The study on the network robustness aims not only for theoretical interests [26][27][28][29][30] but also for practical applications to design more resilient structures against random breakdowns or intentional attacks [31][32][33]. Backup pathway between a pair of nodes is a meaningful concept of the network robustness, captured by the connection between a pair through at least two paths, termed biconnectivity [34,35].…”

Section: Introductionmentioning

confidence: 99%

Network robustness of multiplex networks with interlayer degree correlations

et al. 2014

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We study the robustness properties of multiplex networks consisting of multiple layers of distinct types of links, focusing on the role of correlations between degrees of a node in different layers. We use generating function formalism to address various notions of the network robustness relevant to multiplex networks such as the resilience of ordinary-and mutual connectivity under random or targeted node removals as well as the biconnectivity. We found that correlated coupling can affect the structural robustness of multiplex networks in diverse fashion. For example, for maximallycorrelated duplex networks, all pairs of nodes in the giant component are connected via at least two independent paths and network structure is highly resilient to random failure. In contrast, anticorrelated duplex networks are on one hand robust against targeted attack on high-degree nodes, but on the other hand they can be vulnerable to random failure.

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Section: Introductionmentioning

confidence: 99%

Network robustness of multiplex networks with interlayer degree correlations

et al. 2014

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“…Since the networks are embedded in the external environment, they are not immune to the possibilities of node and/or link failure or their intentional removal. Structural robustness of networks has been, therefore, one of the main issues in network science [1][2][3][4][5][6][7][8]. Among many possibilities of structure alteration to malfunction of a network, random failure that uniformly occurs on every node and selective node removal from those with large degrees (degree-based targeted attack) are the two main categories, to which this article also pay attention.…”

Section: Introductionmentioning

confidence: 99%

Robustness analysis of bimodal networks in the whole range of degree correlation

Mizutaka

Tanizawa

2016

Phys. Rev. E

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We present exact analysis of the physical properties of bimodal networks specified by the two peak degree distribution fully incorporating the degree-degree correlation between node connection. The structure of the correlated bimodal network is uniquely determined by the Pearson coefficient of the degree correlation, keeping its degree distribution fixed. The percolation threshold and the giant component fraction of the correlated bimodal network are analytically calculated in the whole range of the Pearson coefficient from −1 to 1 against two major types of node removal, which are the random failure and the degree-based targeted attack. The Pearson coefficient for next-nearestneighbor pairs is also calculated, which always takes a positive value even when the correlation between nearest-neighbor pairs is negative. From the results, it is confirmed that the percolation threshold is a monotonically decreasing function of the Pearson coefficient for the degrees of nearestneighbor pairs increasing from −1 and 1 regardless of the types of node removal. In contrast, the node fraction of the giant component for bimodal networks with positive degree correlation rapidly decreases in the early stage of random failure, while that for bimodal networks with negative degree correlation remains relatively large until the removed node fraction reaches the threshold. In this sense, bimodal networks with negative degree correlation are more robust against random failure than those with positive degree correlation.

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“…In [12], Callaway et al have introduced the concept of percolation process [13] and applied it to examine the resilience of various real world networks like Internet. In [14], researchers have addressed a more realistic scenario in which a network is subjected to simultaneous targeted and random attacks.…”

Section: Introductionmentioning

confidence: 99%

Stability analysis of peer-to-peer networks against churn

et al. 2008

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Abstract. Users of the peer-to-peer system join and leave the network randomly, which makes the overlay network dynamic and unstable in nature. In this paper, we propose an analytical framework to assess the robustness of p2p networks in the face of user churn. We model the peer churn through degree-independent as well as degree-dependent node failure. Lately, superpeer networks are becoming the most widely used topology among the p2p networks. Therefore, we perform the stability analysis of superpeer networks as a case study. We validate the analytically derived results with the help of simulation.

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Optimization of network robustness to waves of targeted and random attacks

Cited by 181 publications

References 19 publications

Network robustness of multiplex networks with interlayer degree correlations

Network robustness of multiplex networks with interlayer degree correlations

Robustness analysis of bimodal networks in the whole range of degree correlation

Stability analysis of peer-to-peer networks against churn

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