Identifying influential nodes in weighted networks based on evidence theory

Wei, Daijun; Deng, Xinyang; Zhang, Xiaoge; Deng, Yong; Mahadevan, Sankaran

doi:10.1016/j.physa.2013.01.054

Cited by 192 publications

(124 citation statements)

References 36 publications

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“…In [11], the authors brought in webpage grade vectors in PageRank algorithm to rank the nodes and thus evaluated the node importance. In [12], the authors proposed an evidence-theory-based measurement which could weigh both the degree and strength of every node in a weighted network. The node influence could be represented by basic probability assignment (BPA), and the measure in this paper is determined by the combination of BPAs.…”

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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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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“…We define the transmission efficiency of link eij between nodes i and j as λij representing the amount of data transmitted correctly by link eij per unit time. According to [6], [13], for the initial transmission efficiency we have…”

Section: International Journal Of Computer and Communication Engineeringmentioning

confidence: 99%

Model of Cascading Failures for Communication Networks

Zhu¹,

Liu²,

Yu³

et al. 2016

IJCCE

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Abstract:Communication networks play such a critical role in modern society that any failures in these networks are intolerable. In this study, we focus on the influence of network congestion and build a model to study the dynamics of cascading failures in communication networks. Investigation of the relationship between the initial allocation of efficiency among edges and the damage caused by cascading failures shows that a higher initial efficiency may lead to more severe consequence in case of cascading failures. We also investigate the influence of the size of the router's buffer. Because the correct buffer size can alleviate network congestion to some extent, it is able to enhance the robustness of the network against cascading failures.

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“…It has extended the Dempster-Shafer theory [35,36]. DempsterShafer theory is with advantages to handle uncertain information [37,38,39,40,41,42,43,44], and is extensively used in many fields, such as risk assessment [45,46], expert systems [47,48], classification and clustering [49,50], parameter estimation [51], decision making [52,53,54], and so forth [55,56,57,58,59,60,61]. However, there are some weaknesses in DempsterShafer theory.…”

Section: Numbersmentioning

confidence: 99%

D-CFPR: D numbers extended consistent fuzzy preference relations

Deng

Chan

et al. 2015

Knowledge-Based Systems

Self Cite

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How to express an expert's or a decision maker's preference for alternatives is an open issue. Consistent fuzzy preference relation (CFPR) is with big advantages to handle this problem due to it can be construed via a smaller number of pairwise comparisons and satisfies additive transitivity property.However, the CFPR is incapable of dealing with the cases involving uncertain and incomplete information. In this paper, a D numbers extended consistent fuzzy preference relation (D-CFPR) is proposed to overcome the weakness.

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Identifying influential nodes in weighted networks based on evidence theory

Cited by 192 publications

References 36 publications

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

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

Model of Cascading Failures for Communication Networks

D-CFPR: D numbers extended consistent fuzzy preference relations

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