Enhancing network robustness against targeted and random attacks using a memetic algorithm

Tang, Xianglong; Liu, Jing; Zhou, Mingxing

doi:10.1209/0295-5075/111/38005

Cited by 24 publications

(9 citation statements)

References 44 publications

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“…For instance, in reality it is necessary to repair or recover a damaged network, thus existing studies can be categorized as: network robustness under attacks and network robustness under recoveries [33,41,42]. While many studies make efforts to answer the question of how robust a network is in face of perturbations, another research direction explores answers towards questions like what are the optimal structures of a network that are robust to perturbations and what kind of measures can be taken to enhance the robustness of a network [25,34,40,[43][44][45].…”

Section: Previous Workmentioning

confidence: 99%

Breakup of Directed Multipartite Networks

Cai

Pratama

Alam

et al. 2020

IEEE Trans. Netw. Sci. Eng.

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A complex network in reality often consists of profuse components each of which may suffer from unpredictable perturbations. Because the components of a network could be interdependent, therefore the failures of some components may trigger catastrophes to the whole network. It is thus pivotal to exploit the robustness of complex networks to perturbations. Existing studies on network robustness mainly deal with interdependent or multilayer networks, little work is done to investigate the robustness of multipartite networks which are an indispensable part of complex networks. Here we plumb the robustness of directed multipartite networks. To be specific, we exploit the robustness of bi-directed and unidirectional multipartite networks in face of random node failures. We respectively establish cascading and non-cascading models based on the largest connected component concept for depicting the dynamical processes on bi-directed and unidirectional multipartite networks subject to random node attacks. Based on our developed models, we respectively derive the corresponding percolation theories for mathematicaly computing the robustness of directed multipartite networks to random node failures. We theoretically unravel the first-order and second-order phase transition phenomena on the robustness of directed multipartite networks. The correctness of our developed theories coincide quite well with simulations on computer-generated multipartite networks.

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Section: Previous Workmentioning

confidence: 99%

Breakup of Directed Multipartite Networks

Cai

Pratama

Alam

et al. 2020

IEEE Trans. Netw. Sci. Eng.

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“…Random: This algorithm chooses 𝑏 nodes randomly from the set of all nodes in the graph. The random strategy has been used in the past to enhance network robustness [25].…”

Section: Heuristics We Describe the Heuristics Belowmentioning

confidence: 99%

Network Robustness via Global k-cores

Dey¹,

Maity²,

Medya³

et al. 2020

Preprint

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Network robustness is a measure a network's ability to survive adversarial attacks. But not all parts of a network are equal. Kcores, which are dense subgraphs, are known to capture some of the key properties of many real-life networks. Therefore, previous work has attempted to model network robustness via the stability of its 𝑘-core. However, these approaches account for a single core value and thus fail to encode a global network resilience measure. In this paper, we address this limitation by proposing a novel notion of network resilience that is defined over all cores. In particular, we evaluate the stability of the network under node removals with respect to each node's initial core. Our goal is to compute robustness via a combinatorial problem: find 𝑏 most critical nodes to delete such that the number of nodes that fall from their initial cores is maximized. One of our contributions is showing that it is NPhard to achieve any polynomial factor approximation of the given objective. We also present a fine-grained complexity analysis of this problem under the lens of parameterized complexity theory for several natural parameters. Moreover, we show two applications of our notion of robustness: measuring the evolution of species and characterizing networks arising from different domains.

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“…where the symbol r q denotes the number of remaining nodes in a network when removing q nodes from the network. The R n index is straightforward and proved to be effective for measuring the robustness of complex networks [11], [44], [45].…”

Section: ) the R N Metricmentioning

confidence: 99%

A Probabilistic Theory Based Method for Robustness Assessment of Bipartite Networks

Liu

et al. 2019

IEEE Access

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Complex networks play a significant role in our daily lives. Due to the fact that complex networks, in reality, could suffer from manifold perturbations and network components could break down, it is, therefore, pivotal to investigate the robustness of complex networks in the face of perturbations. In the literature, enormous endeavors have been made to study network robustness. However, existing studies mainly deal with the interdependent networks, while little attention is paid to study the robustness of bipartite networks. Many networks, such as economical networks and biological networks, can be modeled as bipartite networks. It is therefore important to study the robustness of bipartite networks. Because the structures of bipartite networks differ from that of networks in common sense, existing robustness analysis methods cannot be applied to bipartite networks. With regard to this, this paper proposes a generic method based on the probabilistic theory to assess the robustness of bipartite networks under random node failures. The proposed method mathematically indicates that bipartite networks are robust to random perturbations. The experiments on random bipartite Erdös-Rényi and scale-free networks demonstrate that the theoretical results yielded by the proposed method coincide quite well with the simulation results. This paper also compares the proposed method against the well-studied method. The experiments indicate that the proposed method outperforms the comparison method.INDEX TERMS Bipartite networks, network robustness, graph theory, largest connected component, phase transition.

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Enhancing network robustness against targeted and random attacks using a memetic algorithm

Cited by 24 publications

References 44 publications

Breakup of Directed Multipartite Networks

Breakup of Directed Multipartite Networks

Network Robustness via Global k-cores

A Probabilistic Theory Based Method for Robustness Assessment of Bipartite Networks

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