Ranking important nodes in complex networks by simulated annealing

Sun, Yu; Peiyang, Yao; Wan, Lujun; Shen, Jian; Zhong, Yun

doi:10.1088/1674-1056/26/2/020201

Cited by 7 publications

(4 citation statements)

References 22 publications

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“…Here, we selected a common multi-objective decision method with limited options, which is, namely, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm. The TOPSIS algorithm is currently widely used due to its superiority in the evaluation results [54][55][56], but we need to consider the data-processing problem and the weight of each index.…”

Section: Identification and Ranking Of Node Importancementioning

confidence: 99%

Dynamic Evolution Analysis of Complex Topology and Node Importance in Shenzhen Metro Network from 2004 to 2021

Meng

Liu

et al. 2022

Sustainability

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With the prosperous development of the urban metro network, the characteristics of the topological structure and node importance are changing dynamically. Most studies focus on static comparisons, and dynamic evolution research is rarely conducted. It is necessary to track the dynamic evolution mechanism of the metro network from the perspective of development. In this paper, the Shenzhen Metro Network (SZMN) topology from 2004 to 2021 was first modeled in Space L. Five kinds of node centralities in eight periods were measured. Then, the dynamic evolution characteristics of the SZMN network topology and node centralities were compared. Finally, an improved multi-attribute decision-making method (MADM) was used to evaluate the node importance, and the spatiotemporal-evolution mechanism of the node importance was discussed qualitatively and quantitatively. The results show that, with the spatiotemporal evolution of the SZMN, the nodes became more and more intensive, and the network tended to be assortative. The different kinds of node centralities changed variously over time. Moreover, the node importance of the SZMN gradually dispersed from the core area of Chegongmiao–Futian to the direction of the Airport and Shenzhen North. The node importance evolves dynamically over time, and it is closely related to the changes in the node type, surrounding nodes and whole network environment. This study reveals the dynamic evolution mechanism of the complex topology and node importance in the SZMN, which can provide scientific suggestions and decision support for the planning, construction, operation management and resilient sustainable development of the urban metro.

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Section: Identification and Ranking Of Node Importancementioning

confidence: 99%

Dynamic Evolution Analysis of Complex Topology and Node Importance in Shenzhen Metro Network from 2004 to 2021

Meng

Liu

et al. 2022

Sustainability

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“…[ 75 ], while the organic and aqueous extracts from the fruits, leaves and roots have shown antimicrobial properties [ 76 ]. The plant is also claimed to possess anticancer [ 77 ], anticholinergic [ 78 ], antifilarial [ 79 ], anti-malarial [ 80 ], CNS depressant and stimulant [ 81 ], hypoglycemic effect [ 82 ], immunologic effect [ 83 ], smooth muscle relaxant and stimulant activity [ 84 ] and other important activities [ 85 ]. Kostova and Dinchev have previously published a review on the chemical diversity of saponins isolated from this species [ 86 ].…”

Section: Thymelaeaceae and Tribulaceaementioning

confidence: 99%

The Chemistry and Biological Activities of Natural Products from Northern African Plant Families: From Taccaceae to Zygophyllaceae

Ntie‐Kang

Njume

Malange

et al. 2016

Nat. Prod. Bioprospect.

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Traditional medicinal practices have a profound influence on the daily lives of people living in developing countries, particularly in Africa, since the populations cannot generally afford the cost of Western medicines. We have undertaken to investigate the correlation between the uses of plants in Traditional African medicine and the biological activities of the derived natural products, with the aim to validate the use of traditional medicine in Northern African communities. The literature is covered for the period 1959–2015 and part III of this review series focuses on plant families with names beginning with letters T to Z. The authors have focused on curating data from journals in natural products and phytomedicine. Within each journal home page, a query search based on country name was conducted. All articles “hits” were then verified, one at a time, that the species was harvested within the Northern African geographical regions. The current data partly constitutes the bases for the development of the Northern African natural compounds database. The review discusses 284 plant-based natural compounds from 34 species and 11 families. It was observed that the ethnobotanical uses of less than 40 % of the plant species surveyed correlated with the bioactivities of compounds identified.Graphical AbstractElectronic supplementary materialThe online version of this article (doi:10.1007/s13659-016-0091-9) contains supplementary material, which is available to authorized users.

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“…Locating influential nodes helps us to understand and control the processes such as epidemic disease outbreak, [5] virus propagation [6] and rumor spreading, [7] and it also plays an important role in viral marketing or accelerating information or knowledge diffusion. [8][9][10][11] So far, many achievements have been made in searching vital nodes and a variety of effective methods have been proposed. Among them, degree centrality (DC), betweenness centrality (BC) and closeness centrality (CC) are three classical measures.…”

Section: Introductionmentioning

confidence: 99%

LCH: A local clustering H-index centrality measure for identifying and ranking influential nodes in complex networks*

Xu¹,

Meng²,

Tu³

et al. 2021

Chinese Phys. B

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Identifying influential nodes in complex networks is one of the most significant and challenging issues, which may contribute to optimizing the network structure, controlling the process of epidemic spreading and accelerating information diffusion. The node importance ranking measures based on global information are not suitable for large-scale networks due to their high computational complexity. Moreover, they do not take into account the impact of network topology evolution over time, resulting in limitations in some applications. Based on local information of networks, a local clustering H-index (LCH) centrality measure is proposed, which considers neighborhood topology, the quantity and quality of neighbor nodes simultaneously. The proposed measure only needs the information of first-order and second-order neighbor nodes of networks, thus it has nearly linear time complexity and can be applicable to large-scale networks. In order to test the proposed measure, we adopt the susceptible-infected-recovered (SIR) and susceptible-infected (SI) models to simulate the spreading process. A series of experimental results on eight real-world networks illustrate that the proposed LCH can identify and rank influential nodes more accurately than several classical and state-of-the-art measures.

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Ranking important nodes in complex networks by simulated annealing

Cited by 7 publications

References 22 publications

Dynamic Evolution Analysis of Complex Topology and Node Importance in Shenzhen Metro Network from 2004 to 2021

Dynamic Evolution Analysis of Complex Topology and Node Importance in Shenzhen Metro Network from 2004 to 2021

The Chemistry and Biological Activities of Natural Products from Northern African Plant Families: From Taccaceae to Zygophyllaceae

LCH: A local clustering H-index centrality measure for identifying and ranking influential nodes in complex networks*

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