A modified weighted TOPSIS to identify influential nodes in complex networks

Hu, Jiantao; Du, Yuxian; Mo, Hongming; Wei, Daijun; Deng, Yong

doi:10.1016/j.physa.2015.09.028

Cited by 113 publications

(51 citation statements)

References 48 publications

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“…The technique for order preference by similarity to ideal solution (TOPSIS), which proposed by Hwang et al, is an MCDM method used in conception and application. The standard TOPSIS method aims to select alternatives that have the shortest distance from the positive ideal solution and the negative ideal solution simultaneously . The positive ideal solution maximizes the benefit criteria and minimizes the cost criteria, whereas the negative ideal solution minimizes the benefit criteria and maximizes the cost criteria .…”

Section: Preliminariesmentioning

confidence: 99%

Failure mode and effects analysis based on D numbers and TOPSIS

Bian

Zheng

Yin

et al. 2018

Quality & Reliability Eng

Self Cite

175

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Failure mode and effects analysis (FMEA) is a widely used technique for assessing the risk of potential failure modes in designs, products, processes, system, and services. One of the main problems with FMEA is the need to address a variety of assessments given by FMEA team members and the sequence of the failure modes according to the degree of risk factors. Many different methods have been proposed to improve the traditional FMEA, which is impractical when the risk assessments given by multiple experts to one failure mode are imprecise, incomplete, or inconsistent. However, the existing methods cannot adequately handle these types of uncertainties. In this paper, a new risk priority model based on D numbers and technique for the order of preference by similarity to ideal solution (TOPSIS) is proposed to evaluate the risk in FMEA. In the proposed model, the assessments given by the FMEA team members are represented by D numbers, where a new feasible and effective method can effectively represent the uncertain information. The TOPSIS method, a multicriteria decision‐making method is presented to rank the preference of failure modes with respect to risk factors. Finally, an application of the failure modes of the rotor blades of an aircraft turbine is provided to illustrate the efficiency of the proposed method.

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Section: Preliminariesmentioning

confidence: 99%

Failure mode and effects analysis based on D numbers and TOPSIS

Bian

Zheng

Yin

et al. 2018

Quality & Reliability Eng

Self Cite

175

View full text Add to dashboard Cite

show abstract

“…The weight of the criteria was computed using AHP method and TOPSIS method was applied to obtain the rank list of nodes based on the importance of nodes. An improved TOPSIS method was proposed by D et al [18] that considered multiple criteria for the identification of influential nodes in the network. The shortcoming of the approach was the way of computation of weights for criteria.…”

Section: IImentioning

confidence: 99%

Identification of Influential Node in a Complex Network using WSM (Weighted Sum Model)

Ahmad¹

2019

IJRASET

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Identification of influential nodes is a significant issue in the structural analysis of complex network. To address this issue, different centrality measures have been proposed such as betweenness centrality, closeness centrality, degree centrality, but all of them suffered from some drawbacks. This research proposes a new method to identify influential nodes based on Weighted Sum Method (WSM). Weighted Sum Method is one of the widely used and simplest multi-criteria decision making method. In this paper, different centrality measures are considered as the multi-attribute of complex network, so as to take advantage of each centrality measure. The multi-attribute of complex network are aggregated using WSM to compute the importance of each node in the network. The efficacy and feasibility of the proposed method is established by conducting experiments on four real-world networks and an informative network.

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“…A new technique [11] known as Technique for Order Preferences was presented by Similarity to Ideal Solution (TOPSIS weighted) to improve the ranking of node spread. With this method, the authors in [12] not only considered different centrality measures as the multi-attribute to the network but also proposed a new algorithm to calculate the weight of each attribute; and to evaluate its performance in four real networks they used the Susceptible-Infected-Recovered (SIR) model to do the simulation. Hu, et al's experiments on four real networks showed that the proposed method could rank the spreading ability of nodes more accurately than the original method.…”

Section: Related Workmentioning

confidence: 99%

Identifying Top-k Most Influential Nodes by using the Topological Diffusion Models in the Complex Networks

Paidar¹,

Chaharborj²,

Harounabadi³

2017

ijacsa

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Social networks are subset of complex networks, where users are defined as nodes, and the connections between users are edges. One of the important issues concerning social network analysis is identifying influential and penetrable nodes. Centrality is an important method among many others practiced for identification of influential nodes. Centrality criteria include degree centrality, betweenness centrality, closeness centrality, and Eigenvector centrality; all of which are used in identifying those influential nodes in weighted and weightless networks. TOPSIS is another basic and multi-criteria method which employs four criteria of centrality simultaneously to identify influential nodes; a fact that makes it more accurate than the above criteria. Another method used for identifying influential or top-k influential nodes in complex social networks is Heat Diffusion Kernel: As one of the Topological Diffusion Models; this model identifies nodes based on heat diffusion. In the present paper, to use the topological diffusion model, the social network graph is drawn up by the interactive and non-interactive activities; then, based on the diffusion, the dynamic equations of the graph are modeled. This was followed by using improved heat diffusion kernels to improve the accuracy of influential nodes identification. After several re-administrations of the topological diffusion models, those users who diffused more heat were chosen as the most influential nodes in the concerned social network. Finally, to evaluate the model, the current method was compared with Technique for Order Preferences by Similarity to Ideal Solution (TOPSIS).

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A modified weighted TOPSIS to identify influential nodes in complex networks

Cited by 113 publications

References 48 publications

Failure mode and effects analysis based on D numbers and TOPSIS

Failure mode and effects analysis based on D numbers and TOPSIS

Identification of Influential Node in a Complex Network using WSM (Weighted Sum Model)

Identifying Top-k Most Influential Nodes by using the Topological Diffusion Models in the Complex Networks

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