New Link Attack Strategies of Complex Networks Based on <i>k</i>-Core Decomposition

Sun, Shiwen; Liu, Huan; Wang, Li; Xia, Chengyi

doi:10.1109/tcsii.2020.2973668

Cited by 20 publications

(7 citation statements)

References 23 publications

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“…Link removal (LR), also known as bond percolation [75], link attack [108,119], or link pruning [120], studies how the robustness of networks decreases by removing links [108].…”

Section: Link Removalmentioning

confidence: 99%

Considering weights in real social networks: A review

et al. 2023

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Network science offers powerful tools to model complex social systems. Most social network science research focuses on topological networks by simply considering the binary state of the links, i.e., their presence or absence. Nonetheless, complex social systems present heterogeneity in link interactions (link weight), and accounting for this heterogeneity, it is mandatory to design reliable social network models. Here, we revisit the topic of weighted social networks (WSNs). By summarizing the main notions, findings, and applications in the field of WSNs, we outline how WSN methodology may improve the modeling of several real problems in social sciences. We are convinced that WSNs may furnish ideas and insights to open interesting lines of new research in the social sciences.

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“…Link removal (LR), also known as bond percolation [75], link attack [108,119], or link pruning [120], studies how the robustness of networks decreases by removing links [108].…”

Section: Link Removalmentioning

confidence: 99%

Considering weights in real social networks: A review

et al. 2023

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“…For the first step, we analyze the time complexity of degree, k-core, and betweenness centrality of nodes. e calculation of node degree is straightforward, and its time complexity is related to the number of nodes n, that is, the complexity is O(log(N)) [46]. e k-core has a low computational complexity with O(M) [46], where M stands for the links in the network.…”

Section: Time Complexity Of Dbc-ahpmentioning

confidence: 99%

“…e calculation of node degree is straightforward, and its time complexity is related to the number of nodes n, that is, the complexity is O(log(N)) [46]. e k-core has a low computational complexity with O(M) [46], where M stands for the links in the network. According to a fast method of calculating the betweenness centrality, the complexity of the unweighted graph is O(MN) [47].…”

Section: Time Complexity Of Dbc-ahpmentioning

confidence: 99%

Control Mechanism of Virus Propagation in Wireless Sensor Networks Based on Analytic Hierarchy Process

Jiang

Pan

et al. 2022

Security and Communication Networks

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The research on virus propagation process and control method in wireless sensor networks (WSNs) is one of the essential challenges of network security. This paper proposes a virus control mechanism of degree, betweenness centrality, and k-core-based analytical hierarchy process (DBC-AHP) for WSNs. According to the topology of WSNs, the virus control mechanism uses the DBC-AHP to identify the crucial nodes of the network. It uses the way of crucial nodes’ self-disconnection to suppress the spread of the virus, to improve the network security. In this paper, the effectiveness of the virus control mechanism based on the DBC-AHP is verified by comparing and analyzing the effect of four different crucial node recognition algorithms. With the research of virus control mechanisms in various network environments, it is found that the average degree of nodes, the communication radius of nodes, and the probability of virus infection can affect the inhibition effect of the virus control mechanism. Furthermore, the inhibition effect of virus control mechanisms is studied under the condition with/without MAC mechanism.

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“…In this article, in order to prevent the link attack and the third‐party server from being broken, when sending information, the user sends the anchor rather than his own specific location 19 . Therefore, it is necessary to select the anchor point first, that is, to select the nearest base point of the Voronoi diagram, and calculate the anchor point with the Euclidean distance.…”

Section: Preliminariesmentioning

confidence: 99%

k‐anonymity based location privacy protection method for location‐based services in Internet of Thing

Wang

Guo

et al. 2021

Concurrency and Computation

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The wide application of Internet of Things technology has promoted the application of location‐based services (LBS). Users can enjoy various conveniences brought by LBS. However, mobile users should submit their location information to LBS which may lead to privacy disclosure. Therefore, users need to protect their privacy while enjoying the service. If the privacy protection problem cannot be solved, the development of mobile internet business will be greatly affected. The existing location privacy protection methods take little account of real‐time road conditions and are unable to resist link attacks. In this article, a k‐anonymous location privacy protection method based on Voronoi map was proposed. First, the Voronoi diagram was divided according to road network structure and real‐time road network data, and Voronoi unit was established with the intersection as the base point. Then, the base point is sent as an anchor instead of the user's actual location to the third‐party server. The Voronoi diagram generated by the k‐anonymity constituted an anonymous space to resist multi‐query attacks and protected the location. k‐1 user location points that are communicating were selected to form k‐anonymous set with the current user, and send the anonymous set to LBS. Theoretical analysis and experimental results show that the proposed method not only efficiently protected location privacy, but also provided high‐quality service.

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New Link Attack Strategies of Complex Networks Based on k-Core Decomposition

Cited by 20 publications

References 23 publications

Considering weights in real social networks: A review

Considering weights in real social networks: A review

Control Mechanism of Virus Propagation in Wireless Sensor Networks Based on Analytic Hierarchy Process

k‐anonymity based location privacy protection method for location‐based services in Internet of Thing

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