The largest α-sepctral radius of k-uniform bicyclic hypergraphs

Let G be a k-uniform hypergraph and A? (G) = ?D(G)+(1-?)A(G) the convex linear combination of its degree diagonal tensor D(G) and its adjacency tensor A(G), where k ? 3 and 0 ? ? < 1. The ?-spectral radius of G is the largest modulus of all the eigenvalues of A? (G). Let B(n, k) be the set of the connected k-uniform bicyclic hypergraphs, where k ? 3. The number of the edges of the hypergraphs in B(n, k) is denoted by m=n+1/k-1. We develop a new ??-normal labeling method for calculating the ?-spectral radius of k-uniform hypergraphs. By using some transformations and the new ??-normal labeling methods, we characterize the hypergraphs with the first and the second largest ?-spectral radii among B(n, k), where k ? 4 and m = n+1/k?1 ? 20.

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Publication Details

Journal
Filomat
Published
2025-01-01
DOI
https://doi.org/10.2298/fil2509861y
Primary Topic
Tensor decomposition and applications
Type
article
Field-Weighted Citation Impact
0.00

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article

The largest α-sepctral radius of k-uniform bicyclic hypergraphs

Lou-Jun Yu, W. Wang
Filomat
Tensor decomposition and applications
article

The largest α-sepctral radius of k-uniform bicyclic hypergraphs

Lou-Jun Yu, W. Wang
article en

Abstract

Let G be a k-uniform hypergraph and A? (G) = ?D(G)+(1-?)A(G) the convex linear combination of its degree diagonal tensor D(G) and its adjacency tensor A(G), where k ? 3 and 0 ? ? < 1. The ?-spectral radius of G is the largest modulus of all the eigenvalues of A? (G). Let B(n, k) be the set of the connected k-uniform bicyclic hypergraphs, where k ? 3. The number of the edges of the hypergraphs in B(n, k) is denoted by m=n+1/k-1. We develop a new ??-normal labeling method for calculating the ?-spectral radius of k-uniform hypergraphs. By using some transformations and the new ??-normal labeling methods, we characterize the hypergraphs with the first and the second largest ?-spectral radii among B(n, k), where k ? 4 and m = n+1/k?1 ? 20.

FilomatVol. 39(9)
Shanghai University (CN), New York University Shanghai (CN)
Natural Science Foundation of Shanghai, National Natural Science Foundation of China
Openalex Percentile: Top 79%
Tensor decomposition and applications
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