Results 21 to 30 of about 263,474 (283)
Centrality Learning: Auralization and Route Fitting
Entropy, 2023 Developing a tailor-made centrality measure for a given task requires domain- and network-analysis expertise, as well as time and effort. Thus, automatically learning arbitrary centrality measures for providing ground-truth node scores is an important ...
Xin Li, Liav Bachar, Rami Puzis
doaj +1 more source
Robustness of Network Measures to Link Errors [PDF]
, 2013 In various applications involving complex networks, network measures are employed to assess the relative importance of network nodes. However, the robustness of such measures in the presence of link inaccuracies has not been well characterized.
Girvan, Michelle, Ott, Ed, Platig, John
core +1 more source
Adjustable reach in a network centrality based on current flows
, 2021 Centrality, which quantifies the "importance" of individual nodes, is among the most essential concepts in modern network theory. Most prominent centrality measures can be expressed as an aggregation of influence flows between pairs of nodes.
Gurfinkel, Aleks J., Rikvold, Per Arne
core +1 more source
Redundancy of Centrality Measures in Financial Market Infrastructures
Latin American Journal of Central Banking, 2023 The concept of centrality is widely used to monitor systems with a network structure because it allows identifying their most influential participants. This monitoring task can be difficult if the number of system participants is considerably large or if
Constanza Martínez-Ventura +2 more
doaj +1 more source
On the limiting behavior of parameter-dependent network centrality measures [PDF]
, 2015 We consider a broad class of walk-based, parameterized node centrality measures for network analysis. These measures are expressed in terms of functions of the adjacency matrix and generalize various well-known centrality indices, including Katz and ...
Benzi, Michele, Klymko, Christine
core +3 more sources
Flowthrough Centrality: A Stable Node Centrality Measure
Journal of Data Science, 2022 This paper introduces flowthrough centrality, a node centrality measure determined from the hierarchical maximum concurrent flow problem (HMCFP). Based upon the extent to which a node is acting as a hub within a network, this centrality measure is defined to be the fraction of the flow passing through the node to the total flow capacity of the node ...
Charles F. Mann +3 more
openaire +1 more source
Applied Network Science, 2020 We perform an extensive analysis of how sampling impacts the estimate of several relevant network measures. In particular, we focus on how a sampling strategy optimized to recover a particular spectral centrality measure impacts other topological ...
Nicolò Ruggeri, Caterina De Bacco
doaj +1 more source
Measuring node centrality when local and global measures overlap
Physical Review E, 2022 Centrality metrics aim to identify the most relevant nodes in a network. In literature, a broad set of metrics exists, either measuring local or global centrality characteristics. Nevertheless, when networks exhibit a high spectral gap, the usual global centrality measures typically do not add significant information with respect to the degree, i.e ...
Lorenzo Costantini +3 more
openaire +4 more sources
An Experimental Study on Centrality Measures Using Clustering
Computers, 2021 Graphs can be found in almost every part of modern life: social networks, road networks, biology, and so on. Finding the most important node is a vital issue.
Péter Marjai +2 more
doaj +1 more source
Artificial Intelligence Chemistry, 2023 The proposed work uses centrality measures based heuristic method to improve the efficiency of the solution for the similarity search problem in molecular chemical graphs by effectively identifying central candidate or representative candidate nodes ...
Nirmala Parisutham
doaj +1 more source