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About 10,000 papers for “node distribution”

Node degree distribution.

Isaac Lera (4045058), Toni Pérez (219369), Carlos Guerrero (4045061) et al. · Figshare · 2017

<p>The degree distribution for the three islands for the aggregated 4S. Dashed lines represent fits to <i>f</i>(<i>x</i>) ∼ <i>exp</i>(−<i>bx</i>) with <i>b</i> = (0.005, 0.026, 0.012) for Majorca, Minorca, and Ibiza & Formentera respectively.</p>

Cited by 0Open access

Distribution for node degrees.

Rohit Yadav (8206536), François-Xavier Dupé (17364494), Sylvain Takerkart (5659873) et al. · Figshare · 2023

<p>Degree distribution in the simulated and real population of 137 graphs. The shaded region corresponds to standard deviations across graphs in the population.</p> <p>(PNG)</p>

Cited by 0Open access

Node degree distribution diagram.

Quan Liu (486219), Yuekang Yao (21100674), Meimei Jia (21100677) et al. · Figshare · 2025

The squares represent the distribution in the real dataset and the circles represent the generated dataset.

Cited by 0Open access

Node degree spatial distribution.

Elizabeth N. Davison (682496), Benjamin O. Turner (3360653), Kimberly J. Schlesinger (603200) et al. · Figshare · 2016

<p>Here, the average hyperedge node degree, or number of hyperedges that include each node over all individuals in the multi-task study is plotted on the brain. The scale is logarithmic, and higher values in a region indicate that there are more hyperedges that include the region. There are some regional differences in hyperedge concentration, and the face-specific hypergraph shows a global increase in hyperedges across the brain.</p>

Cited by 0Open access

Distribution of mean node degrees.

Sangyeon Kim (6315776) · Figshare · 2024

<p>The x-axis is log transformed while labels show the raw values. The distribution approximates a normal pattern, with a mean of 7.9 and a maximum mean degree of 84.3, indicating the frequency of chat posts that mention other users.</p>

Cited by 0Open access

Passenger transport urban distribution node

Juan David Calderón Casas · Lumieres - Repositorio institucional Universidad de América · 2019

Given the problem, facing the mobility system in the urban areas of the city, Bogotá Girardot, an intermodal transport is proposed, in the municipality of Soacha, to prevent passenger buses from entering the city of Bogota and thus avoid the collapse of the road structure, which brings with it high CO2 emissions, pirate transport, informal commerce, insecurity. Etc.

Cited by 0

Node degree distribution for Australian network.

Deb Verhoeven (8967983), Katarzyna Musial (8232552), Stuart Palmer (3375662) et al. · Figshare · 2020

<p>In this chart, nodes with node degree higher than 50 took part in one “compendium” project that involved multiple producers and teams.</p>

Cited by 0Open access

Distribution of node degrees of bitcoin.

Wei Zhang (405), Yan Gong (103494), Zhinan Li (13149743) et al. · Figshare · 2025

<div> With the rapid expansion of non-customized data assets, developing reliable and objective methods for their valuation has become essential. However, current evaluation techniques often face challenges such as incomplete indicator systems and an over-reliance on subjective judgment. To address these issues, this study presents a structured framework comprising 17 key indicators for assessing data asset value. A neural network is employed to calculate indicator weights, which reduces subjectivity and enhances the accuracy of the assessment. Additionally, knowledge graph techniques are used to organize and visualize relationships among the indicators, providing a comprehensive evaluation view. The proposed model combines information entropy and the TOPSIS method to refine asset valuation by integrating indicator weights and performance metrics. To validate the model, it is applied to two datasets: Bitcoin market data from the past seven years and BYD stock data. The Bitcoin dataset demonstrates the model’s capability to capture market trends and assess purchasing potential, while the BYD stock dataset highlights its adaptability across diverse financial assets. The successful application of these cases confirms the model’s effectiveness in supporting data-driven asset management and pricing. This framework provides a systematic methodology for data asset valuation, offering significant theoretical and practical implications for asset pricing and management. </div>

Cited by 0Open access

The node distribution for meshless methods

C. Hérault, V. Leconte, Y. Maréchal et al. · Springer Link (Chiba Institute of Technology) · 2001

\n \n Meshless methods are new numerical simulation methods. \nMeshless methods are based on a simple set of nodes that has to be optimized\nto obtain a good convergence of the approximation.\nFor these reasons, in this paper, we have developed a new procedure in order\nto generate the initial set of nodes. This approach does not rely on any \nexisting meshing technique, such as Delaunay or advancing front. It is based \non a map density of nodes and a regularization procedure. \n \n\n

Cited by 0

Distribution of node betweenness of bitcoin.

Wei Zhang (405), Yan Gong (103494), Zhinan Li (13149743) et al. · Figshare · 2025

<div> With the rapid expansion of non-customized data assets, developing reliable and objective methods for their valuation has become essential. However, current evaluation techniques often face challenges such as incomplete indicator systems and an over-reliance on subjective judgment. To address these issues, this study presents a structured framework comprising 17 key indicators for assessing data asset value. A neural network is employed to calculate indicator weights, which reduces subjectivity and enhances the accuracy of the assessment. Additionally, knowledge graph techniques are used to organize and visualize relationships among the indicators, providing a comprehensive evaluation view. The proposed model combines information entropy and the TOPSIS method to refine asset valuation by integrating indicator weights and performance metrics. To validate the model, it is applied to two datasets: Bitcoin market data from the past seven years and BYD stock data. The Bitcoin dataset demonstrates the model’s capability to capture market trends and assess purchasing potential, while the BYD stock dataset highlights its adaptability across diverse financial assets. The successful application of these cases confirms the model’s effectiveness in supporting data-driven asset management and pricing. This framework provides a systematic methodology for data asset valuation, offering significant theoretical and practical implications for asset pricing and management. </div>

Cited by 0Open access