Results 241 to 250 of about 197,953 (292)
Evaluating global measures of network centralization: Axiomatic and numerical assessments. [PDF]
PLoS OneSaberi M, Aref S.
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Reply to Koplenig and Wolfer: Global language analyses must account for relationships, location, and unbalanced binary data. [PDF]
Proc Natl Acad Sci U S AHua X, Bromham L.
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Associations between micro- and macro level social network properties and individual productivity in virtual collaboration. [PDF]
Sci RepDeng D, Koltai J.
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Task-related differences in network connectivity and dynamics in people with severe opioid use disorder compared with healthy controls. [PDF]
Transl PsychiatryKurtin DL +8 more
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Three Hypergraph Eigenvector Centralities
SIAM Journal on Mathematics of Data Science, 2019 Eigenvector centrality is a standard network analysis tool for determining the importance of (or ranking of) entities in a connected system that is represented by a graph. However, many complex systems and datasets have natural multi-way interactions that are more faithfully modeled by a hypergraph.
Austin R Benson
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BIT Numerical Mathematics, 2003
The authors investigate the conditions under which it is possible to estimate and compute error bounds on a computed eigenvector of a finite matrix. It is shown that nontrivial error bounds on an eigenvector are computable if and only if its geometric multiplicity is one. They also provide an algorithm for the computation of these error bounds and show
Rump, Siegfried M., Zemke, Jens-Peter M.
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The authors investigate the conditions under which it is possible to estimate and compute error bounds on a computed eigenvector of a finite matrix. It is shown that nontrivial error bounds on an eigenvector are computable if and only if its geometric multiplicity is one. They also provide an algorithm for the computation of these error bounds and show
Rump, Siegfried M., Zemke, Jens-Peter M.
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Extended eigenvalue–eigenvector method
Statistics & Probability Letters, 2022zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kataria, K. K., Khandakar, M.
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2007
Eigenvalues and the associated eigenvectors of an endomorphism of a vector space are defined and studied, as is the spectrum of an endomorphism. The characteristic polynomial of a matrix is considered and used to define the characteristic polynomial of the endomorphism of a finitely-generated vector space.
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Eigenvalues and the associated eigenvectors of an endomorphism of a vector space are defined and studied, as is the spectrum of an endomorphism. The characteristic polynomial of a matrix is considered and used to define the characteristic polynomial of the endomorphism of a finitely-generated vector space.
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2009
Radiative flux transfer between Lambertian surfaces can be described in terms of linear resistive networks with voltage sources. This thesis examines how these "radiative transfer networks" provide a physical interpretation for the eigenvalues and eigenvectors of form factor matrices. This leads to a novel approach to photorealistic image synthesis and
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Radiative flux transfer between Lambertian surfaces can be described in terms of linear resistive networks with voltage sources. This thesis examines how these "radiative transfer networks" provide a physical interpretation for the eigenvalues and eigenvectors of form factor matrices. This leads to a novel approach to photorealistic image synthesis and
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1997
Gaussian elimination plays a fundamental role in solving a system Ax = b of linear equations. In order to solve a system of linear equations, Gaussian elimination reduces the augmented matrix to a (reduced) row-echelon form by using elementary row operations that preserve row and null spaces.
Jin Ho Kwak, Sungpyo Hong
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Gaussian elimination plays a fundamental role in solving a system Ax = b of linear equations. In order to solve a system of linear equations, Gaussian elimination reduces the augmented matrix to a (reduced) row-echelon form by using elementary row operations that preserve row and null spaces.
Jin Ho Kwak, Sungpyo Hong
openaire +1 more source