Influential spreaders identification in complex networks with potential edge weight based k-shell degree neighborhood method

Maji, Giridhar

doi:10.1016/j.jocs.2019.101055

Cited by 46 publications

(10 citation statements)

References 37 publications

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“…For the CN under the status of active, the risks would propagate beyond the local level to the entire network, which analogizes the current within the electric circuit, and the risk flow model is proposed in this study to assess the criticality of the nodes involved in the CN. To verify the correctness of the abovementioned approaches, the SIR simulation is employed in this study as the benchmark because the satisfied accuracy of SIR simulation for criticality evaluation has been proved (Maji, 2020; Wang et al., 2020).…”

Section: Principle Of the Novel Methodology To Conduct A Risk Assessmentmentioning

confidence: 99%

“…The status of CN can be described by being active and nonactive for the risk propagation within the objective CN which are dependent on different scenarios. Under the status of nonactive, the risk propagation among nodes within the CN is limited to local level, in which the criticality of the nodes can be evaluated by the k-shell decomposition algorithm due to its applicability of coping with local information (Maji, 2020). For the CN under the status of active, the risks would propagate beyond the local level to the entire network, which analogizes the current within the electric circuit, and the risk flow model is proposed in this study to assess the criticality of the nodes involved in the CN.…”

Section: Criticality Evaluation For the Risk Eventsmentioning

confidence: 99%

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A novel methodology concentrating on risk propagation to conduct a risk analysis based on a directed complex network

Deng

Qiao

et al. 2022

Risk Analysis

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A novel methodology is proposed in the present study to describe the risk propagation process by quantitatively evaluating the criticality and sensitivity of risk events according to complex network theory, based on which risk matrices are developed to interrupt the risk propagation process by setting up safety barriers. The applicability and accuracy of the improved k-shell decomposition algorithm and risk flow model for calculating the criticality proposed in this study are verified by the susceptible-infectedrecovered (SIR) simulation, which is widely regarded as a benchmark for complex networks (CN) issues. The results confirm the advantages of the proposed methodologies considering comprehensively various comparison indicators. The sensitivity of the nodes is quantified by running an SIR simulation with a variable infection rate and recovery rate. Finally, the criticality and sensitivity of risk events contribute to the development of risk matrices with three different risk scenarios, based on which the applicability and effectiveness of safety barriers are qualitatively analyzed to interrupt the risk propagation process. The framework and methodologies proposed in this study could well present the risk propagation process within CNs and are proven to have a great potential for studies on safety barriers.

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Section: Principle Of the Novel Methodology To Conduct A Risk Assessmentmentioning

confidence: 99%

Section: Criticality Evaluation For the Risk Eventsmentioning

confidence: 99%

A novel methodology concentrating on risk propagation to conduct a risk analysis based on a directed complex network

Deng

Qiao

et al. 2022

Risk Analysis

View full text Add to dashboard Cite

show abstract

“…The representative methods based on neighborhood centrality include degree centrality (DC for short) [ 23 ], K-shell decomposition [ 24 – 28 ], and H-index [ 29 ] methods. DC holds the view that a node with more neighbors has a greater influence [ 23 ].…”

Section: Related Workmentioning

confidence: 99%

A mechanics model based on information entropy for identifying influencers in complex networks

Xiao

2023

Appl Intell

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The network, with some or all characteristics of scale-free, self-similarity, self-organization, attractor and small world, is defined as a complex network. The identification of significant spreaders is an indispensable research direction in complex networks, which aims to discover nodes that play a crucial role in the structure and function of the network. Since influencers are essential for studying the security of the network and controlling the propagation process of the network, their assessment methods are of great significance and practical value to solve many problems. However, how to effectively combine global information with local information is still an open problem. To solve this problem, the generalized mechanics model is further improved in this paper. A generalized mechanics model based on information entropy is proposed to discover crucial spreaders in complex networks. The influence of each neighbor node on local information is quantified by information entropy, and the interaction between each node on global information is considered by calculating the shortest distance. Extensive tests on eleven real networks indicate the proposed approach is much faster and more precise than traditional ways and state-of-the-art benchmarks. At the same time, it is effective to use our approach to identify influencers in complex networks.

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“…Then, to measure the influence power of each node, they calculated the sum of the weights of all edges connected to that node. Maji [ 25 ], In a similar work to [ 24 ], However, instead of adjusting parameters, used a measure based on the network’s average degree and K-Shell and a combination of a K-Shell index and degree of nodes to weigh the graph edges.…”

Section: Related Workmentioning

confidence: 99%

A multi-attribute method for ranking influential nodes in complex networks

et al. 2022

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Calculating the importance of influential nodes and ranking them based on their diffusion power is one of the open issues and critical research fields in complex networks. It is essential to identify an attribute that can compute and rank the diffusion power of nodes with high accuracy, despite the plurality of nodes and many relationships between them. Most methods presented only use one structural attribute to capture the influence of individuals, which is not entirely accurate in most networks. The reason is that network structures are disparate, and these methods will be inefficient by altering the network. A possible solution is to use more than one attribute to examine the characteristics aspect and address the issue mentioned. Therefore, this study presents a method for identifying and ranking node’s ability to spread information. The purpose of this study is to present a multi-attribute decision making approach for determining diffusion power and classification of nodes, which uses several local and semi-local attributes. Local and semi-local attributes with linear time complexity are used, considering different aspects of the network nodes. Evaluations performed on datasets of real networks demonstrate that the proposed method performs satisfactorily in allocating distinct ranks to nodes; moreover, as the infection rate of nodes increases, the accuracy of the proposed method increases.

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Influential spreaders identification in complex networks with potential edge weight based k-shell degree neighborhood method

Cited by 46 publications

References 37 publications

A novel methodology concentrating on risk propagation to conduct a risk analysis based on a directed complex network

A novel methodology concentrating on risk propagation to conduct a risk analysis based on a directed complex network

A mechanics model based on information entropy for identifying influencers in complex networks

A multi-attribute method for ranking influential nodes in complex networks

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