Results 81 to 90 of about 1,271 (139)

On the Boundary of Incidence Energy and Its Extremum Structure of Tricycle Graphs

Frontiers in Physics, 2020
With the wide application of graph theory in circuit layout, signal flow chart and power system, more and more attention has been paid to the network topology analysis method of graph theory.
Hongyan Lu, Zhongxun Zhu
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On the Signless Laplacian ABC-Spectral Properties of a Graph

Mathematics
In the paper, we introduce the signless Laplacian ABC-matrix Q̃(G)=D¯(G)+Ã(G), where D¯(G) is the diagonal matrix of ABC-degrees and Ã(G) is the ABC-matrix of G. The eigenvalues of the matrix Q̃(G) are the signless Laplacian ABC-eigenvalues of G.
Bilal A. Rather, Hilal A. Ganie, Yilun Shang   +2 more
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What is a proper graph Laplacian? An operator-theoretic framework for graph diffusion

Special Matrices
We introduce an operator-theoretic definition of a proper graph Laplacian as any matrix associated with a given graph that can be expressed as the composition of a divergence and a gradient operator, with the gradient acting between graph-related spaces ...
Estrada Ernesto
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On spectrum and energies of enhanced power graphs

Mathematics Open
The enhanced power graph [Formula: see text] of a group G is a simple graph with vertex set G and two distinct vertex are adjacent if and only if they belong to the same cyclic subgroup.
Pankaj Kalita, Prohelika Das
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The Aα-Spectral Radii of Graphs with Given Connectivity

Mathematics, 2019
The A α -matrix is A α ( G ) = α D ( G ) + ( 1 − α ) A ( G ) with α ∈ [ 0 , 1 ] , given by Nikiforov in 2017, where A ( G ) is adjacent matrix, and D ( G ) is its ...
Chunxiang Wang, Shaohui Wang
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Representation learning of in-degree-based digraph with rich information

Complex & Intelligent Systems
Network representation learning aims to map the relationship between network nodes and context nodes to a low-dimensional representation vector space. Directed network representation learning considers mapping directional of node vector.
Yan Sun, Cun Zhu, JianFu Chen, Kejia Lan, Jiuchang Pei   +4 more
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Some sufficient conditions on hamilton graphs with toughness. [PDF]

Front Comput Neurosci, 2022
Cai G, Yu T, Xu H, Yu G.
europepmc   +1 more source

Quantitative structure-properties relationship analysis of Eigen-value-based indices using COVID-19 drugs structure. [PDF]

Int J Quantum Chem, 2023
Rauf A, Naeem M, Hanif A.
europepmc   +1 more source

Comparison of Different Properties of Graph Using Adjacency Matrix and Signless Laplacian Matix

International Journal of Scientific Research in Science, Engineering and Technology
This study highlights the advantages of using the Signless Laplacian spectrum over the traditional Adjacency matrix spectrum for graph representation. It demonstrates that the Signless Laplacian possesses greater representational power and stronger characterization properties, making it a more effective tool for analyzing graph structures. Particularly,
null Km. Priti Sahrawat, null Dr. Ashish Kumar   +1 more
openaire   +1 more source

Signless laplacian spectral characterization of roses

Kuwait Journal of Science, 2020
A p-rose graph Γ = RG(a3, a4, . . . , as) is a graph consisting of p =a3 + a4 + · · · + as ≥ 2 cycles that all meet in one vertex, and ai (3 ≤ i ≤ s) is the number of cycles in Γ of length i.
ALI ZEYDI ABDIAN, ALI REZA ASHRAFI, Maurizion Brunetti   +2 more
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