The Self-Information Weighting-Based Node Importance Ranking Method for Graph Data

Liu, Shihu; H, Gao

doi:10.3390/e24101471

Cited by 3 publications

(4 citation statements)

References 47 publications

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“…The above table shows the results of weight calculation of entropy weight method and the weight of each indicator is analyzed according to the results. The final pricing decision for each category of vegetables was obtained by weighting the total sales volume of each category based on the weights calculated by the TOPSIS method [5].…”

Section: Volumementioning

confidence: 99%

Research on Vegetable Incoming and Pricing Strategies Based on Support Vector Machines and Gray Prediction Models

2023

infse

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This study analyzes vegetable stocking and pricing decisions based on market demand and historical transaction data with the aim of maximizing the profit of fresh produce supermarkets. A mathematical model was constructed using descriptive statistical analysis, cluster analysis, Pearson correlation analysis, gray prediction model, entropy weight TOPSIS method and support vector machine (SVR) regression. The study performed correlation analysis on vegetable category and specific product data and found a negative correlation between similar vegetable varieties; then it used gray prediction and TOPSIS method to predict the vegetable sales volume in the coming week and combined with SVR regression model to predict the pricing. Finally, the time series model was used to develop further pricing strategies for specific high-margin vegetable items. This study provides dedicated support for scientific decision-making on vegetable sales, as well as a reference for pricing and replenishment decisions on related items.

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Section: Volumementioning

confidence: 99%

Research on Vegetable Incoming and Pricing Strategies Based on Support Vector Machines and Gray Prediction Models

2023

infse

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“…Airports were ranked according to the connectivity results to identify the most and least accessible areas of the air traffic network in India. Lina Hao et al [28] proposed a network planning model based on the idea of the shortest path. They established a road network evaluation index for path planning which can reduce the running time of the train and improve the running efficiency.…”

Section: Research On Hsr Network Planningmentioning

confidence: 99%

“…Equation (23) indicates that the main backbone line planning has the highest urgency among all lines in the network. Equation (26) indicates that at least one of the backbone lines in the online network should be the starting point of the selected city i; Equation (27) shows that at least one of the backbone lines in the online network should be the end of the selected city j; Equation (28) indicates that there is at most one planned route between cities i and j; Equations ( 26)-( 28) ensure the continuity of the alignment and geographical location of the backbone line.…”

Section: Main Backbone Line Planning Modelmentioning

confidence: 99%

Urban Agglomeration High-Speed Railway Backbone Network Planning: A Case Study of Beijing-Tianjin-Hebei Region, China

2023

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In order to optimize the network layout of urban agglomerations, improve the comprehensive benefits of transportation networks and promote the sustainable development of urban agglomerations, this paper studies the main trunk line selection model of the Beijing–Tianjin–Hebei high-speed railway (HSR). Firstly, the characteristics of cities in urban agglomeration are analyzed, and the economic capacity, transportation capacity, passenger turnover and network characteristics of urban nodes are selected as evaluation indexes. A node importance model and a line urgency model were established to obtain the value of the importance of urban nodes and the urgency of each line in the urban agglomeration. Secondly, the DBSCAN is used to cluster the city nodes, and the city nodes are divided into four grades. With the goal of maximizing the urgency of the lines and considering the constraints of the urban node level, the optimization model of the Beijing–Tianjin–Hebei backbone network selection is constructed. The backbone lines of the Beijing–Tianjin–Hebei urban agglomeration are obtained, and the selection results of backbone lines are analyzed, which lays a foundation for the design and optimization of the HSR operation scheme in urban agglomeration. The planned backbone network can basically realize the commuting between the important urban nodes in the Beijing–Tianjin–Hebei urban agglomeration to achieve the goal of driving and alleviating the operation of the branch line. It can accelerate the development of the internal traffic of the urban agglomeration. In addition, it has certain practical significance and practical value.

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“…So far, for the key node identification problem of single-layer networks, a variety of identification methods have been proposed for specific problems. These methods can be classified according to their essential ideas, including eigenvectorsbased method 9 , node removal shrinkage-based method 10 , and graph entropy theory-based method 11 . Considering that identification methods based on a single attribute may ignore other characteristics, a variety of key node identification methods based on multi-attribute fusion have also been proposed 12 .…”

Section: Introductionmentioning

confidence: 99%

Key node identification for a network topology using hierarchical comprehensive importance coefficients

Qiu,

Yu,

Feng

et al. 2024

Preprint

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Key nodes are similar to important hubs in a network structure, which can directly determine the robustness and stability of the network. By effectively identifying and protecting these critical nodes, the robustness of the network can be improved, making it more resistant to external interference and attacks. There are various topology analysis methods for existing networks, but key node identification methods often only focus on local attributes or global attributes. Designing an algorithm that combines both attributes can improve the accuracy of key node identification. In this paper, the constraint coefficient of a weakly connected network is calculated based on the Salton indicator, and the hierarchical tenacity global coefficient is obtained by an improved K-Shell decomposition method. Then, a hierarchical comprehensive node importance identification algorithm is proposed which can comprehensively indicate the local and global attributes of the network nodes. Experimental results on real network datasets show that the proposed algorithm outperforms the other classic algorithms in terms of connectivity, average remaining edges, sensitivity and monotonicity.

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The Self-Information Weighting-Based Node Importance Ranking Method for Graph Data

Cited by 3 publications

References 47 publications

Research on Vegetable Incoming and Pricing Strategies Based on Support Vector Machines and Gray Prediction Models

Research on Vegetable Incoming and Pricing Strategies Based on Support Vector Machines and Gray Prediction Models

Urban Agglomeration High-Speed Railway Backbone Network Planning: A Case Study of Beijing-Tianjin-Hebei Region, China

Key node identification for a network topology using hierarchical comprehensive importance coefficients

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