Critical Nodes Identification in Complex Networks

Yang, Haihua; An, Shi

doi:10.3390/sym12010123

Cited by 38 publications

(14 citation statements)

References 42 publications

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“…The experiments are performed using a PC Intel (R) Core i7-8750H CPU @ 2.20GHz, with memory (RAM) 16 In each figure, pv represents the CNN-RP predicted curve; tv represents the true curve obtained by attack simulations; ξ represents the prediction error that can be calculated by Eq. (3); and σ represents the standard deviation of the testing data that are randomly collected.…”

Section: Experimental Studiesmentioning

confidence: 99%

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A Convolutional Neural Network Approach to Predicting Network Connectedness Robustness

Lou

et al. 2021

IEEE Trans. Netw. Sci. Eng.

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To quantitatively measure the connectedness robustness of a complex network, a sequence of values that record the remaining connectedness of the network after a sequence of node-or edge-removal attacks can be used. However, it is computationally time-consuming to measure the network connectedness robustness by attack simulations for large-scale networked systems. In the present paper, an efficient method based on convolutional neural network (CNN) is proposed to train for estimating the network connectedness robustness. The new approach is motivated by the facts that 1) the adjacency matrix of a network can be converted to a gray-scale image and CNN is very powerful for image processing, and 2) CNN has proved very effective in predicting the controllability robustness of complex networks. Extensive experimental studies on directed and undirected, as well as synthetic and real-world networks suggest that: 1) the proposed CNN-based methodology performs excellently in the prediction of the connectedness robustness of complex networks as a process; 2) it performs fairly well as the indicator for the connectedness robustness, compared to other predictive measures.

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Section: Experimental Studiesmentioning

confidence: 99%

“…Among the existing centrality measures, degree and betweenness are the most frequently used two [12], [13]. Besides centrality, other commonly-used measures of importance include neighborhood similarity [14], branch weighting [15], and structural holes [16].…”

mentioning

confidence: 99%

A Convolutional Neural Network Approach to Predicting Network Connectedness Robustness

Lou

et al. 2021

IEEE Trans. Netw. Sci. Eng.

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“…The authors derived a complete Taylor expansion of the first-passage time distribution between two arbitrary states for a general Markov model using a combination of algebraic methods and graph theory approaches. Yang & An (2020) [29] research revealed that critical nodes identification in complex networks was significant for studying the survivability and robustness of networks. The authors leveraged the property of structural hole to design a heuristic algorithm based on local information of the network topology.…”

Section: Organized Terrorist Networkmentioning

confidence: 99%

Computational Robotics: An Alternative Approach for Predicting Terrorist Networks

Nwanga¹,

Okafor

Chukwudebe

et al. 2021

Int. J. Robot. Autom. Technol.

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Increasing terrorist activities globally have attracted the attention of many researchers, policy makers and security agencies towards counterterrorism. The clandestine nature of terrorist networks have made them difficult for detection. Existing works have failed to explore computational characterization to design an efficient threat-mining surveillance system. In this paper, a computationally-aware surveillance robot that auto-generates threat information, and transmit same to the cloud-analytics engine is developed. The system offers hidden intelligence to security agencies without any form of interception by terrorist elements. A miniaturized surveillance robot with Hidden Markov Model (MSRHMM) for terrorist computational dissection is then derived. Also, the computational framework for MERHMM is discussed while showing the adjacency matrix of terrorist network as a determinant factor for its operation. The model indicates that the terrorist network have a property of symmetric adjacency matrix while the social network have both asymmetric and symmetric adjacency matrix. Similarly, the characteristic determinant of adjacency matrix as an important operator for terrorist network is computed to be -1 while that of a symmetric and an asymmetric in social network is 0 and 1 respectively. In conclusion, it was observed that the unique properties of terrorist networks such as symmetric and idempotent property conferred a special protection for the terrorist network resilience. Computational robotics is shown to have the capability of utilizing the hidden intelligence in attack prediction of terrorist elements. This concept is expected to contribute in national security challenges, defense and military intelligence.

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“…In the research of complex networks, it is found that a small number of nodes play a key role [21,22]. They have an irreplaceable role in network performance, and often determine the structure and function of the network.…”

Section: Multi-indicators Node Importance Evaluation Based On Partial Ordermentioning

confidence: 99%

“…The coreness of a node can indicate the depth of the node in the core. The concept of coreness is given by Batagelj [21]: given a graph, by removing all the nodes with degrees less than k and their corresponding edge, get a remaining sub-graph, called k-core. If a node belongs to k-core and does not belong to (k+1)-core, then it has a coreness of k. The coreness of a node is a global property based on the location of the network.…”

Section: • Coreness Centralitymentioning

confidence: 99%

Identifying Influential SLD Authoritative Name Servers on the Internet

Zhang

Han

et al. 2021

Front. Phys.

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DNS plays an important role on the Internet. The addressing of most applications depends on the proper operation of DNS. The root servers and the top-level domain servers are relied upon by many domains on the Internet, and their security affects the whole Internet. As a result, more attention has been paid to the security of servers at these two levels. However, the security of second-level domains and their servers also needs to be brought to the forefront. This paper focuses on showing the complex resolving dependencies and identifying influential name servers for second-level domains. We start by detecting domain name resolution paths and building up a name dependency graph. Then we construct domain name resolution networks of different numbers and sizes, which are connected by a certain number of domain name resolution graphs. On this basis, the network is analyzed from the perspective of complex network analysis, and a multi-indicators node importance evaluation method based on partial order is proposed to identify the influential name servers of the network. Once these name servers are not properly configured and fail or are compromised by DDoS attacks, it will cause resolution failure for a wide range of domain names.

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Critical Nodes Identification in Complex Networks

Cited by 38 publications

References 42 publications

A Convolutional Neural Network Approach to Predicting Network Connectedness Robustness

A Convolutional Neural Network Approach to Predicting Network Connectedness Robustness

Computational Robotics: An Alternative Approach for Predicting Terrorist Networks

Identifying Influential SLD Authoritative Name Servers on the Internet

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