tnet » Weighted Networks » Node Centrality The centrality of nodes, or the identification of which nodes are more "central" than others, has been a key issue in network analysis (Freeman, 1978; Bonacich, 1987; Borgatti, 2005; Borgatti et al., 2006)
Wasserman, S., Faust, K., 1994. Social Network Analysis. Cambridge University Press,Cambridge, MA. Collective dynamics of "small-world" networks. Nature 393, 440-442. Nodecentrality in weighted networks: Generalizing degree and shortest paths.T OpsahlToreopsahl Com...
A few network measures have been proposed for weighted networks, including three common measures of node centrality: degree, closeness, and betweenness. However, these generalizations have solely focused on tie weights, and not on the number of ties, which was the central component of the original...
Laplacian centrality: A new centrality measure for weighted networks The centrality of vertices has been a key issue in network analysis. For unweighted networks where edges are just present or absent and have no weight atta... X Qi,E Fuller,W Qin,... - 《Information Sciences An ...
The concept of controllability from control theory is applied to weighted and directed networks with heterogenous linear or linearized node dynamics subject to exogenous inputs, where the nodes are g...
Building on these ideas, we motivate, define and analyze a class of spectral centrality measures for identifying important nodes and hyperedges in hypergraphs, generalizing existing network science concepts. By exploiting the latest developments in nonlinear Perron−Frobenius theory, we show how the ...
connected nodes tend to have more disparate scores in terms of DomiRank centrality. We demonstrate that the inherent properties of DomiRank make both synthetic and real-world networks particularly fragile to the DomiRank centrality-based attacks, outperforming all other centrality-based attacks. Further...
IDENTIFYING INFLUENTIAL NODES IN DIRECTED WEIGHTED NETWORKS USING PYTHAGOREAN FUZZY SETS 2024 Little Lion Scientific. All rights reserved.Centrality considers node importance in complex networks, addressing this issue poses a significant challe... VR Songa,P Bodapati - 《Journal of Theoretical & Applied...
The networks used in the experiment allow for coexisting paths with a different number of intermediaries, where theory predicts both efficient and inefficient (Nash) equilibria. Furthermore, these networks present different characteristics, such as degree and centrality distributions, that may affect the...
method integrating node centrality with time series to appraise the impact of common neighbors in dynamic networks, and to capture the evolving pattern of node centrality over time. The proficiency of graph convolutional networks in learning intricate systems has prompted some researchers to develop repr...