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Computing the determinant of a signed graph
A signed graph is a simple graph in which every edge has a positive or negative sign. In this article, we employ several algebraic techniques to compute the determinant of a signed graph in terms of the spectrum of a vertex-deleted subgraph.
Alshamary Bader, Stanić Zoran
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WSGMB: weight signed graph neural network for microbial biomarker identification. [PDF]
Pan S, Jiang X, Zhang K.
europepmc +1 more source
An upper bound for the Laplacian index of a signed graph [PDF]
Farzaneh Ramezani, Zoran Stanic
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AbstractSigned graphs are assigned to systems of points in a metric space. Special cases are investigated.
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Edge coloring of small signed graphs
In 2020, Behr introduced the problem of edge coloring of signed graphs and proved that every signed graph (G, sigma) can be colored using Delta(G) or Delta(G) + 1 colors, where Delta(G) denotes the maximum degree of G.
Robert Janczewski +2 more
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Kernelized multiview signed graph learning for single-cell RNA sequencing data. [PDF]
Karaaslanli A +3 more
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Signed graph representation learning for functional-to-structural brain network mapping. [PDF]
Tang H +9 more
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KS-CMI: A circRNA-miRNA interaction prediction method based on the signed graph neural network and denoising autoencoder. [PDF]
Wang XF +7 more
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Eigenpairs of adjacency matrices of balanced signed graphs
In this article, we study eigenvalues λ\lambda and their associated eigenvectors xx of the adjacency matrices AA of balanced signed graphs. Balanced signed graphs were first introduced and studied by Harary to handle a problem in social psychology ...
Chen Mei-Qin
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