Amplitude Bound in Ladder Graph Models. II

1964 ◽  
Vol 136 (4B) ◽  
pp. B1217-B1220 ◽  
Author(s):  
George Tiktopoulos ◽  
S. B. Treiman
Keyword(s):  
Graph Models ◽  
Ladder Graph

Domination integrity of some graph classes

2019 ◽  
Vol 53 (5) ◽  
pp. 1721-1728
Author(s):  
Ayse Besirik ◽  
Elgin Kilic
Keyword(s):  
Communication Network ◽  
Network Design ◽  
Dominating Set ◽  
Graph Models ◽  
Graph Classes ◽  
Ladder Graph ◽  
Wheel Graph ◽  
Vulnerability Measure ◽  
The Stability ◽  
Thorn Graph

The stability of a communication network has a great importance in network design. There are several vulnerability measures used to determine the resistance of network to the disruption in this sense. Domination theory provides a model to measure the vulnerability of a graph network. A new vulnerability measure of domination integrity was introduced by Sundareswaran in his Ph.D. thesis (Parameters of vulnerability in graphs (2010)) and defined as DI(G) = min{|S| + m(G − S):S ∈ V(G)} where m(G − S) denotes the order of a largest component of graph G − S and S is a dominating set of G. The domination integrity of an undirected connected graph is such a measure that works on the whole graph and also the remaining components of graph after any break down. Here we determine the domination integrity of wheel graph W1,n, Ladder graph Ln, Sm,n, Friendship graph Fn, Thorn graph of Pn and Cn which are commonly used graph models in network design.

Knowledge representation in graph-models of complex technical systems

2020 ◽  
pp. 12-16
Author(s):  
Evgenia R. Muntyan
Keyword(s):  
Analytical Study ◽  
Level Structure ◽  
Graph Models ◽  
Level Data ◽  
Knowledge Formation ◽  
Different Types ◽  
Level Information ◽  
Complex Technical Systems ◽  
Technical Systems ◽  
Structure Of Knowledge

The article analyzes a number of methods of knowledge formation using various graph models, including oriented, undirected graphs with the same type of edges and graphs with multiple and different types of edges. This article shows the possibilities of using graphs to represent a three-level structure of knowledge in the field of complex technical systems modeling. In such a model, at the first level, data is formed in the form of unrelated graph vertices, at the second level – information presented by a related undirected graph, and at the third level – knowledge in the form of a set of graph paths. The proposed interpretation of the structure of knowledge allows to create new opportunities for analytical study of knowledge and information, their properties and relationships.

Networks

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Biological Networks ◽  
Generative Models ◽  
The Internet ◽  
Network Data ◽  
Computer Algorithms ◽  
Graph Models ◽  
Random Graph Models ◽  
The Social ◽  
Spectral Algorithms ◽  
Wide Availability

The study of networks, including computer networks, social networks, and biological networks, has attracted enormous interest in recent years. The rise of the Internet and the wide availability of inexpensive computers have made it possible to gather and analyse network data on an unprecendented scale, and the development of new theoretical tools has allowed us to extract knowledge from networks of many different kinds. The study of networks is broadly interdisciplinary and developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social science. This book brings together the most important breakthroughts in each of these fields and presents them in a unified fashion, highlighting the strong interconnections between work in different areas. Topics covered include the measurement of networks; methods for analysing network data, including methods developed in physics, statistics, and sociology; fundamentals of graph theory; computer algorithms, including spectral algorithms and community detection; mathematical models of networks such as random graph models and generative models; and models of processes taking place on networks.

scholarly journals On metric chromatic number of comb product of ladder graph

2021 ◽  
Vol 1836 (1) ◽  
pp. 012026
Author(s):  
M Y Rohmatulloh ◽  
Slamin ◽  
A I Kristiana ◽  
Dafik ◽  
R Alfarisi
Keyword(s):  
Chromatic Number ◽  
Ladder Graph

scholarly journals Exponential random graph models for little networks

2021 ◽  
Vol 64 ◽  
pp. 225-238
Author(s):  
George G. Vega Yon ◽  
Andrew Slaughter ◽  
Kayla de la Haye
Keyword(s):  
Random Graph ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Random Graph Models ◽  
Exponential Random Graph
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