A new method of identifying influential nodes in complex networks based on TOPSIS

Du, Yuxian; Gao, Cai; Hu, Yong; Mahadevan, Sankaran; Deng, Yong

doi:10.1016/j.physa.2013.12.031

Cited by 177 publications

(88 citation statements)

References 53 publications

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“…Recently, Du et al [23] firstly introduced TOPIS as a mew measure of centrality. Gao et al [24] improved the original evidential centrality by taking node degree distributions and global structure information into consideration.…”

Section: Literature Reviewmentioning

confidence: 99%

How to Identify the Most Powerful Node in Complex Networks? A Novel Entropy Centrality Approach

Qiao

Shan

Zhou

2017

Entropy

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Centrality is one of the most studied concepts in network analysis. Despite an abundance of methods for measuring centrality in social networks has been proposed, each approach exclusively characterizes limited parts of what it implies for an actor to be "vital" to the network. In this paper, a novel mechanism is proposed to quantitatively measure centrality using the re-defined entropy centrality model, which is based on decompositions of a graph into subgraphs and analysis on the entropy of neighbor nodes. By design, the re-defined entropy centrality which describes associations among node pairs and captures the process of influence propagation can be interpreted explained as a measure of actor potential for communication activity. We evaluate the efficiency of the proposed model by using four real-world datasets with varied sizes and densities and three artificial networks constructed by models including Barabasi-Albert, Erdos-Renyi and Watts-Stroggatz. The four datasets are Zachary's karate club, USAir97, Collaboration network and Email network URV respectively. Extensive experimental results prove the effectiveness of the proposed method.

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Section: Literature Reviewmentioning

confidence: 99%

How to Identify the Most Powerful Node in Complex Networks? A Novel Entropy Centrality Approach

Qiao

Shan

Zhou

2017

Entropy

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“…Secondary vertical shock absorber V 13 Railway coupling V 14 Gearbox V 15 Grounding device V 16 Traction motor V 17 Height adjusting device V 18 Antihunting damper V 19 Air spring V 20 Center pin bush V 21 Traction rod V 22 Transverse shock absorber V 23 Transverse backstop V 24 Anti-side-rolling torsion bar V 25 Control valve V 26 Speed Sensor 1 V 27 Speed Sensor 2 V 28 LKJ2000 V 29 Device for cleaning the tread band of vehicle wheels V 30 Acceleration sensor V 31 Junction box V 32 Temperature sensor bearing V 33 Axle temperature sensor…”

Section: A Case Study and Discussionmentioning

confidence: 99%

“…In some cases, using different centrality measures may provide different results, even conflicting results [26]. MCDM has been proposed to address this problem.…”

Section: Problem Descriptionmentioning

confidence: 99%

Component Importance Measure Computation Method Based Fuzzy Integral with Its Application

Lin

Wang

Jia

et al. 2017

Discrete Dynamics in Nature and Society

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In view of the negative impact of component importance measures based on system reliability theory and centrality measures based on complex networks theory, there is an attempt to provide improved centrality measures (ICMs) construction method with fuzzy integral for measuring the importance of components in electromechanical systems in this paper. ICMs are the meaningful extension of centrality measures and component importance measures, which consider influences on function and topology between components to increase importance measures usefulness. Our work makes two important contributions. First, we propose a novel integration method of component importance measures to define ICMs based on Choquet integral. Second, a meaningful fuzzy integral is first brought into the construction comprehensive measure by fusion multi-ICMs and then identification of important components which could give consideration to the function of components and topological structure of the whole system. In addition, the construction method of ICMs and comprehensive measure by integration multi-CIMs based on fuzzy integral are illustrated with a holistic topological network of bogie system that consists of 35 components.

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“…By using multiple indicators to evaluate node importance although these methods can obtain more precise result, there are limitations in these methods with regard to characteristics of the wireless sensor network such as limited energy and operating rate. In [10], the authors proposed a method based on finite number of objects and ideal objective and evaluated the importance of each node in network through TOPSIS technique which is utilized to aggregate the multi-attribute to obtain the result. In [11], the authors brought in webpage grade vectors in PageRank algorithm to rank the nodes and thus evaluated the node importance.…”

Section: Related Workmentioning

confidence: 99%

Node importance evaluation method in wireless sensor network based on energy field model

Sun

Qiao

Wang

et al. 2016

J Wireless Com Network

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The stability degree of key nodes is an important indicator of wireless sensor network performance. Appropriate node importance evaluation method is a precondition for the identification of key node and the analysis on network stability. The current methods based on average length and network density are unable to make real-time evaluation on nodes in practical application. Thus, this paper puts forward a node importance evaluation method in wireless sensor network based on energy field model. Based on the data exchange at the network layer in wireless sensor network, this approach analyzes the node properties and correlation among nodes with energy field and graph theory, which shows the influence result of key nodes in wireless sensor network. Therefore, the node influence can be worked out so as to evaluate the importance of node in wireless sensor network. The experimental results show that, compared with the current node importance evaluation methods of wireless sensor network, the approach proposed in this paper not only is able to evaluate the node importance without affecting the operation of network but also shows the dynamic change in node importance.

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A new method of identifying influential nodes in complex networks based on TOPSIS

Cited by 177 publications

References 53 publications

How to Identify the Most Powerful Node in Complex Networks? A Novel Entropy Centrality Approach

How to Identify the Most Powerful Node in Complex Networks? A Novel Entropy Centrality Approach

Component Importance Measure Computation Method Based Fuzzy Integral with Its Application

Node importance evaluation method in wireless sensor network based on energy field model

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