GFT centrality: A new node importance measure for complex networks

Singh, Rahul; Chakraborty, Abhishek; Manoj, B. S.

doi:10.1016/j.physa.2017.06.018

Cited by 25 publications

(12 citation statements)

References 19 publications

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“…Classical centrality methods include degree centrality and betweenness centrality. On this basis, a large number of scholars proposed improved centrality methods, such as WFCA [ 30 ], GFT centrality [ 31 ], ECP centrality [ 32 ], and eccentricity centrality [ 33 ]. These methods can accurately quantify the importance of nodes in the network from multiple perspectives, such as target node attribute, neighbor node attribute, and path attribute.…”

Section: Methodsmentioning

confidence: 99%

A Key Node Mining Method Based on Acupoint‐Disease Network (ADN): A New Perspective for Exploring Acupoint Specificity

Shi

Liu

Cui

et al. 2020

Evidence-Based Complementary and Alternative Medicine

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In the process of treating pro-diseases with acupuncture, traditional Chinese medicine (TCM) doctors may fine-tune acupuncture prescriptions according to different prior experiences. Different prescriptions will affect the efficiency and effect of acupuncture treatment, and even excessive acupoint selection may cause psychological pressure on patients. We still lack an effective means to analyze the meridian system and acupoint specificity to clarify the mapping relationship between acupoints and diseases. Given the inability of modern medical technology to provide effective evidence support for meridians and acupoints, we combined acupuncture theory with network science for an interdisciplinary discussion. In this paper, we constructed a weighted undirected acupoint-disease network (ADN) based on clinical acupuncture prescription literature and proposed a high-specificity key node mining method based on ADN. Combined with the principle of acupoint selection in TCM, the proposed method balanced the contribution of local areas to the network based on the distribution characteristics of meridians and selected 30 key acupoints with high influence on the global topology according to the evaluation index of key nodes. Finally, we compared the proposed method with the other six classical node importance evaluation algorithms in terms of resolution, network loss, and accuracy. The comprehensive results show that the marked key acupoint nodes make outstanding contributions to the connectivity, topological structure, and weighted benefits of the network, and the stability and specificity of the algorithm guarantee the reliability of the key acupoint nodes. We consider that these key acupoints with high centrality in ADN can be used as core acupoints to help researchers explore targeted and high-impact acupoint combinations under resource constraints and optimize existing acupuncture prescriptions.

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Section: Methodsmentioning

confidence: 99%

A Key Node Mining Method Based on Acupoint‐Disease Network (ADN): A New Perspective for Exploring Acupoint Specificity

Shi

Liu

Cui

et al. 2020

Evidence-Based Complementary and Alternative Medicine

View full text Add to dashboard Cite

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“…The most important node is defined that the number of spanning trees is the smallest after being removed, but the time complexity is not reduced. In reference [14], the graph Fourier transform centrality (GFT-C) is introduced to quantify how important a particular node is to other nodes in a network. GFT-C utilizes not only the local properties, but also the global properties of a network topology.…”

Section: Related Workmentioning

confidence: 99%

Node importance evaluation method based on multi-attribute decision-making model in wireless sensor networks

Yin

Cui

et al. 2019

J Wireless Com Network

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Identifying important nodes is very crucial to design efficient communication networks or contain the spreading of information such as diseases and rumors. The problem is formulated as follows: given a network, which nodes are the more important? Most current studies did not incorporate the structure change as well as application features of a network. Aiming at the node importance evaluation in wireless sensor networks, a new method which ranks nodes according to their structural importance and performance impact is proposed. Namely, this method considers two aspects of the network, network structural characteristics and application requirements. This method integrates four indicators which reflect the node importance, namely, node degree, number of spanning trees, delay, and network energy consumption. Firstly, the changes in the four indicators are analyzed using the node deletion method. Then, the TOPSIS multi-attribute decision-making method is applied to merge these four evaluation indicators. On this basis, a more comprehensive evaluation method (MADME) for node importance is obtained. Theory study reveals MADME method saves computational time. And the simulation results show the superiority of the MADME method over various algorithms such as the N-Burt method, betweenness method, DEL-Node method, and IE-Matrix method. The accuracy of the evaluation can be improved, and the key nodes determined by the MADME method have a more obvious effect on the network performance. Our method can provide guidance on influential node identification in the network.

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“…Equation ( 4) provides a clear description of load transforming probability, which has a positive correlation with the degree of destination node j and current load proportion on origin node i, and has a negative correlation with current load proportion on destination node j. The reason why we give such an equation is that the degree of a node is always used to express the importance of this node in various fields [46]- [48], and it is obvious that if the more load of destination node, and the less empty capacity of the node to accept the new incoming load so the probability is lower; If the origin node has more load proportion, the transforming probability will be increased because more load are badly needed to be transformed. The whole equation is proposed according to the hybrid ant colony algorithm, and the simulation results prove that it is proper and reasonable in real situations.…”

Section: B Denotation Innovation For Crosstalk Influence Parametermentioning

confidence: 99%

Network Congestion Diffusion Model Considering Congestion Distribution Information

2019

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Network congestion diffusion has become the most stubborn disease and scourge of networks. With the help of widely used congestion distribution information, making full use of network capacity dynamically is a feasible and hopeful way to alleviate network congestion diffusion. Existing studies on network congestion diffusion considering congestion distribution information mainly focus on road networks and describe the distribution information of congestion areas with statistical parameters but not take dynamical congestion distribution into account. However, it is difficult to quantify dynamical congestion distribution as it has multiple influencing factors and complex dynamical coupling relationships, and thus there is still a lack of common network congestion diffusion model considering dynamical congestion distribution information. Inspired by the Langevin diffusion model in signal transduction networks, we propose a novel model for common network congestion diffusion considering the influence of dynamical congestion distribution information based on a set of differential equations. In these equations, we quantify the crosstalk influence of dynamical distribution information by a parameter with reference to the routing optimization method in the ant colony algorithm. And, then firstly the complex dynamical coupling network congestion diffusion under the influence of congestion distribution information is analyzed and simulated in a measurable way. The simulation results prove that there are obvious alleviated effects on network congestion diffusion with proper information influence weights, which is shown to be a bathtub curve relationship. Our model provides a simple mathematical approach to discover the relationship between network congestion diffusion and the influence of dynamical congestion distribution information. Based on this relationship, we can relieve network congestion by dynamically adjusting congestion distribution information influence.

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GFT centrality: A new node importance measure for complex networks

Cited by 25 publications

References 19 publications

A Key Node Mining Method Based on Acupoint‐Disease Network (ADN): A New Perspective for Exploring Acupoint Specificity

A Key Node Mining Method Based on Acupoint‐Disease Network (ADN): A New Perspective for Exploring Acupoint Specificity

Node importance evaluation method based on multi-attribute decision-making model in wireless sensor networks

Network Congestion Diffusion Model Considering Congestion Distribution Information

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