Results 11 to 20 of about 40,893 (263)
Centrality measures in networks
Social Choice and Welfare, 2023 We show that prominent centrality measures in network analysis are all based on additively separable and linear treatments of statistics that capture a node's position in the network. This enables us to provide a taxonomy of centrality measures that distills them to varying on two dimensions: (i) which information they make use of about nodes ...
Francis Bloch +2 more
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Comparison-based centrality measures [PDF]
International Journal of Data Science and Analytics, 2021 AbstractRecently, learning only from ordinal information of the type “itemxis closer to itemythan to itemz” has received increasing attention in the machine learning community. Such triplet comparisons are particularly well suited for learning from crowdsourced human intelligence tasks, in which workers make statements about the relative distances in a
Luca Rendsburg, Damien Garreau
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Total communicability as a centrality measure [PDF]
Journal of Complex Networks, 2013 We examine a node centrality measure based on the notion of total communicability, defined in terms of the row sums of the exponential of the adjacency matrix of the network. We argue that this is a natural metric for ranking nodes in a network, and we point out that it can be computed very rapidly even in the case of large networks.
Michele Benzi, Christine Klymko
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The measurement of Central Corneal Thickness
romanian journal of ophthalmology, 2023 We aimed to compare five different devices that measure Central Corneal Thickness. The Central Corneal Thickness (CCT) is an important parameter in ophthalmology. It is involved in the management of various eye conditions such as: glaucoma, keratoconus, contact lens wearing, corneal dystrophies, refractive surgery and keratoplasty.
Camburu, Georgiana +3 more
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Potential gain as a centrality measure [PDF]
IEEE/WIC/ACM International Conference on Web Intelligence, 2019 Navigability is a distinctive features of graphs associated with artificial or natural systems whose primary goal is the transportation of information or goods. We say that a graph $\mathcal{G}$ is navigable when an agent is able to efficiently reach any target node in $\mathcal{G}$ by means of local routing decisions.
De Meo P., Levene M., Provetti A.
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A study of the stability of information and telecommunication networks under conditions of stochastic percolation of nodes [PDF]
Научно-технический вестник информационных технологий, механики и оптики, 2021 In-depth studies of the topological properties of information and telecommunication networks contribute to the understanding of their functional capabilities, including stability.
Fedor L. Shuvaev, Kirill I. Vitenzon
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X-index: A novel flow-based locational measure for quantifying centrality
International Journal of Applied Earth Observations and Geoinformation, 2023 The centrality of a place reflects its relative importance in serving other places, and quantifying centrality is a prerequisite for assessing the urban spatial structure and facilitates policy-making of sustainable urban development.
Xi Wang +7 more
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Scale-dependent measure of network centrality from diffusion dynamics
Physical Review Research, 2020 Classic measures of graph centrality capture distinct aspects of node importance, from the local (e.g., degree) to the global (e.g., closeness). Here we exploit the connection between diffusion and geometry to introduce a multiscale centrality measure. A
Alexis Arnaudon +2 more
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A measure of centrality in cyclic diffusion processes: Walk-betweenness.
PLoS ONE, 2021 Unlike many traditional measures of centrality based on paths that do not allow any repeated nodes or lines, we propose a new measure of centrality based on walks, walk-betweenness, that allows any number of repeated nodes or lines.
Yoosik Youm, Byungkyu Lee, Junsol Kim
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Strategic Evasion of Centrality Measures
International Joint Conference on Autonomous Agents and Multiagent Systems, 2021 Among the most fundamental tools for social network analysis are centrality measures, which quantify the importance of every node in the network. This centrality analysis typically disregards the possibility that the network may have been deliberately manipulated to mislead the analysis. To solve this problem, a recent study attempted to understand how
Waniek, Marcin +5 more
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