The node importance in actual complex networks based on a multi-attribute ranking method

Liu, Zhonghua; Jiang, Cheng; Wang, Juyun; Yu, Hua

doi:10.1016/j.knosys.2015.03.026

Cited by 96 publications

(44 citation statements)

References 26 publications

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“…We conducted the experiments on three real networks and compared the results of our method with the random selection method (Random) and the TOPSIS-RE method in [24]. e experimental result proves that our method performs better.…”

Section: Methodsmentioning

confidence: 99%

“…Different weights are assigned to the erefore, the accuracy is highly dependent on the researchers' personal experience. Similarly, Liu et al [24] proposed a multiattribute ranking method for node importance evaluation in complex networks. ey also select four representative indicators and assign the weights by using Analytic Hierarchy Process.…”

Section: Related Workmentioning

confidence: 99%

“…As can be seen from the definition, K-shell decomposition would assign the same value to all nodes when the network is a Star network or a Tree network. To overcome this challenge, improved K-shell decomposition (IKs) is proposed by Liu et al in [24]. e process of improved K-shell decomposition calculation is illustrated in Figure 1(b).…”

Section: Betweenness Centrality Betweenness Centrality (Bc)mentioning

confidence: 99%

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An Entropy-Based Self-Adaptive Node Importance Evaluation Method for Complex Networks

2020

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Identifying important nodes in complex networks is essential in disease transmission control, network attack protection, and valuable information detection. Many evaluation indicators, such as degree centrality, betweenness centrality, and closeness centrality, have been proposed to identify important nodes. Some researchers assign different weight to different indicator and combine them together to obtain the final evaluation results. However, the weight is usually subjectively assigned based on the researcher’s experience, which may lead to inaccurate results. In this paper, we propose an entropy-based self-adaptive node importance evaluation method to evaluate node importance objectively. Firstly, based on complex network theory, we select four indicators to reflect different characteristics of the network structure. Secondly, we calculate the weights of different indicators based on information entropy theory. Finally, based on aforesaid steps, the node importance is obtained by weighted average method. The experimental results show that our method performs better than the existing methods.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Betweenness Centrality Betweenness Centrality (Bc)mentioning

confidence: 99%

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An Entropy-Based Self-Adaptive Node Importance Evaluation Method for Complex Networks

2020

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“…where A ¼ a ij À Á constitutes the consistency judgment matrix; a b is the given relative importance of the pair of range elements, based on a certain standard, and always generally assigned a constant value of a b = 9 [35].…”

Section: Subjective Indicatorsmentioning

confidence: 99%

A methodology for calculating the passenger comfort benefits of railway travel

Huang

Shen

2018

J. Mod. Transport.

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A comfortable environment in railway passenger coaches can be regarded as a resource for social consumption during the transport process. Railway passenger comfort benefits (RPCBs) can be regarded as a special generalized cost. In this paper, we select a series of objective and subjective indexes to formulate a quantitative method of calculating the RPCB with considering ticket fares. This method includes three steps: make the initial data dimensionless, calculate the weight of each index, and finally calculate the RPCBs. The proposed method was validated with the data collected from two types of trains: G13 from Beijing South to Shanghai Hongqiao and T109 from Beijing to Shanghai. Also, questionnaire survey was conducted in both trains. After data processing, the results show that there is a linear relationship between the RPCB and ticket fare with a correlation coefficient of 0.9616.

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“…ese methods have prompted research to identify the influential nodes in complex networks. However, the computational complexity of these methods is generally high, and the network nodes need to be tentatively tested, which is difficult in practice [11].…”

Section: Introductionmentioning

confidence: 99%

Influential Node Identification in Command and Control Networks Based on Integral k-Shell

Wang

Chen

et al. 2019

Wireless Communications and Mobile Computing

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Influential nodes act as a hub for information transmission in a command and control network. The identification of influential nodes in a network of this nature is a significant and challenging task; however, it is necessary if the invulnerability of the network is to be increased. The existing k-shell method is problematic in that it features a coarse sorting granularity and does not consider the local centrality of nodes. Thus, the degree of accuracy with which the influential nodes can be identified is relatively low. This motivates us to propose a method based on an integral k-shell to identify the influential nodes in a command and control network. This new method takes both the global and local information of nodes into account, introduces the historical k-shell and a 2-order neighboring degree, and refines the k-shell decomposition process in a network. Simulation analysis is carried out from two perspectives: to determine the impact on network performance when influential nodes are removed and to obtain the correlation between the integral k-shell value and its propagation value. The simulation results show that the integral k-shell method, which employs an algorithm of lower complexity, accurately identifies the influence of those nodes with the same k-shell values. Furthermore, the method significantly improves the accuracy with which the influential nodes can be identified.

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The node importance in actual complex networks based on a multi-attribute ranking method

Cited by 96 publications

References 26 publications

An Entropy-Based Self-Adaptive Node Importance Evaluation Method for Complex Networks

An Entropy-Based Self-Adaptive Node Importance Evaluation Method for Complex Networks

A methodology for calculating the passenger comfort benefits of railway travel

Influential Node Identification in Command and Control Networks Based on Integral k-Shell

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