Results 91 to 100 of about 1,618,968 (159)
Computing the reciprocal distance signless Laplacian eigenvalues and energy of graphs [PDF]
In this paper, we study the eigenvalues of the reciprocal distance signless Laplacian matrix of a connected graph and obtain some bounds for the maximum eigenvalue of this matrix.
Ramane, Harishchandra +2 more
core
On Quipus whose signless Laplacian index does not exceed 4.5
Hoffman and Smith proved that in a graph with maximum degree Δ if all edges are subdivided infinitely many times, then the largest eigenvalue, also called index, of the adjacency matrix converges to Δ/(Δ-1).
Wang J. +7 more
core +1 more source
What is a proper graph Laplacian? An operator-theoretic framework for graph diffusion
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
doaj +1 more source
In this paper we consider the graphs having at most two (signless) Laplacian eigenvalues greater than 2. We prove that all these graphs are determined by the spectrum of their Laplacian matrix, and that all these graphs except the star on four vertices ...
Jianfeng Wang +2 more
core
On spectrum and energies of enhanced power graphs
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
doaj +1 more source
Signless Laplacians of finite graphs [PDF]
summary:In this paper, the effects on the signless Laplacian spectral radius of a graph are studied when some operations, such as edge moving, edge subdividing, are applied to the graph.
Bolian Liu +11 more
core +1 more source
Representation learning of in-degree-based digraph with rich information
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 +4 more
doaj +1 more source
The Aα-Spectral Radii of Graphs with Given Connectivity
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
doaj +1 more source
Some sufficient conditions on hamilton graphs with toughness. [PDF]
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]
Rauf A, Naeem M, Hanif A.
europepmc +1 more source

