Spectral Moments of the Edge Adjacency Matrix in Molecular Graphs. 3. Molecules Containing Cycles

1998 ◽  
Vol 38 (1) ◽  
pp. 23-27 ◽  
Author(s):  
Ernesto Estrada
Keyword(s):  
Adjacency Matrix ◽  
Spectral Moments ◽  
Molecular Graphs

The evaluation of spectral moments for molecular graphs of phenylenes

1997 ◽  
Vol 96 (4) ◽  
pp. 256-260 ◽  
Author(s):  
Svetlana Marković ◽  
Aleksandra Stajković
Keyword(s):  
Spectral Moments ◽  
Molecular Graphs

Digraph Energy of Directed Polygons

2020 ◽  
Vol 23 ◽  
Author(s):  
Bo Deng ◽  
Ning Yang ◽  
Weilin Liang ◽  
Xiaoyun Lu
Keyword(s):  
Molecular Orbital ◽  
Adjacency Matrix ◽  
Energy Levels ◽  
Order Relation ◽  
Theoretical Chemistry ◽  
Characteristic Polynomials ◽  
Molecular Graphs ◽  
Conjugated Hydrocarbons ◽  
Quasi Order ◽  
The Absolute

Background: The energy E(G) of G is defined as the sum the absolute values of the eigenvalues of its adjacency matrix. In theoretical chemistry, within the Hu ̈ckel molecular orbital (HMO) approximation, the energy levels of the π-electrons in molecules of conjugated hydrocarbons are related to the energy of the molecular graphs. Objective: Generally, the energy to digraphs was proposed. Methodology: Let Δ_n be the set consisting of digraphs with n vertices and each cycle having length≡2 mod(4). The set of all the n-order directed hollow k-polygons in Δ_n based on a k-polygon G is denoted by H_k (G). Results: In this research, by using the quasi-order relation over Δ_n and the characteristic polynomials of digraphs, we describe the directed hollow k-polygon with the maximum digraph energy in H_k (G). Conclusion: The n-order oriented hollow k-polygon with the maximum digraph energy among Hk(G) only contains a cycle. Moreover, such a cycle is the longest one produced in G.

Bounds for the Eigen Values and Energy of Degree Product Adjacency Matrix of A Graph

2019 ◽  
Vol 10 (3) ◽  
pp. 565-573
Author(s):  
Keerthi G. Mirajkar ◽  
Bhagyashri R. Doddamani
Keyword(s):  
Adjacency Matrix ◽  
Eigen Values

Energy of spherical fuzzy graphs

2020 ◽  
pp. 321-332
Author(s):  
S. Yahya Mohamed ◽  
A. Mohamed Ali
Keyword(s):  
Adjacency Matrix ◽  
Upper Bounds ◽  
Fuzzy Graph ◽  
Lower And Upper Bounds ◽  
Fuzzy Graphs

In this paper, the notion of energy extended to spherical fuzzy graph. The adjacency matrix of a spherical fuzzy graph is defined and we compute the energy of a spherical fuzzy graph as the sum of absolute values of eigenvalues of the adjacency matrix of the spherical fuzzy graph. Also, the lower and upper bounds for the energy of spherical fuzzy graphs are obtained.

Higher-Order and Mixed Discrete Derivatives such as a Novel Graph- Theoretical Invariant for Generating New Molecular Descriptors

2019 ◽  
Vol 19 (11) ◽  
pp. 944-956 ◽  
Author(s):  
Oscar Martínez-Santiago ◽  
Yovani Marrero-Ponce ◽  
Ricardo Vivas-Reyes ◽  
Mauricio E.O. Ugarriza ◽  
Elízabeth Hurtado-Rodríguez ◽  
...  
Keyword(s):  
Molecular Descriptors ◽  
3D Qsar ◽  
Higher Order ◽  
Molecular Structures ◽  
Qsar Modeling ◽  
Statistical Parameters ◽  
Molecular Graphs ◽  
New Family ◽  
Mixed Derivatives ◽  
Discrete Derivative

Background: Recently, some authors have defined new molecular descriptors (MDs) based on the use of the Graph Discrete Derivative, known as Graph Derivative Indices (GDI). This new approach about discrete derivatives over various elements from a graph takes as outset the formation of subgraphs. Previously, these definitions were extended into the chemical context (N-tuples) and interpreted in structural/physicalchemical terms as well as applied into the description of several endpoints, with good results. Objective: A generalization of GDIs using the definitions of Higher Order and Mixed Derivative for molecular graphs is proposed as a generalization of the previous works, allowing the generation of a new family of MDs. Methods: An extension of the previously defined GDIs is presented, and for this purpose, the concept of Higher Order Derivatives and Mixed Derivatives is introduced. These novel approaches to obtaining MDs based on the concepts of discrete derivatives (finite difference) of the molecular graphs use the elements of the hypermatrices conceived from 12 different ways (12 events) of fragmenting the molecular structures. The result of applying the higher order and mixed GDIs over any molecular structure allows finding Local Vertex Invariants (LOVIs) for atom-pairs, for atoms-pairs-pairs and so on. All new families of GDIs are implemented in a computational software denominated DIVATI (acronym for Discrete DeriVAtive Type Indices), a module of KeysFinder Framework in TOMOCOMD-CARDD system. Results: QSAR modeling of the biological activity (Log 1/K) of 31 steroids reveals that the GDIs obtained using the higher order and mixed GDIs approaches yield slightly higher performance compared to previously reported approaches based on the duplex, triplex and quadruplex matrix. In fact, the statistical parameters for models obtained with the higher-order and mixed GDI method are superior to those reported in the literature by using other 0-3D QSAR methods. Conclusion: It can be suggested that the higher-order and mixed GDIs, appear as a promissory tool in QSAR/QSPRs, similarity/dissimilarity analysis and virtual screening studies.

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