scholarly journals Probabilistic graph-theoretic models for dynamic and information capability problems research

2020 ◽  
Vol 971 ◽  
pp. 042001
Author(s):  
Alexander S Geyda
Keyword(s):  
Graph Theoretic ◽  
Probabilistic Graph ◽  
Graph Theoretic Models

Simulating Social Network Formation

2011 ◽  
pp. 581-599
Author(s):  
Robert Gilles ◽  
Tabitha James ◽  
Reza Barkhi ◽  
Dimitrios Diamantaras
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Structure ◽  
Network Formation ◽  
Theoretic Model ◽  
Network Structures ◽  
Agent Behavior ◽  
Graph Theoretic ◽  
Model And Simulation ◽  
Graph Theoretic Models

Social networks depict complex systems as graph theoretic models. The study of the formation of such systems (or networks) and the subsequent analysis of the network structures are of great interest. For information systems research and its impact on business practice, the ability to model and simulate a system of individuals interacting to achieve a certain socio-economic goal holds much promise for proper design and use of cyber networks. We use case-based decision theory to formulate a customizable model of information gathering in a social network. In this model, the agents in the network have limited awareness of the social network in which they operate and of the fixed, underlying payoff structure. Agents collect payoff information from neighbors within the prevailing social network, and they base their networking decisions on this information. Along with the introduction of the decision theoretic model, we developed software to simulate the formation of such networks in a customizable context to examine how the network structure can be influenced by the parameters that define social relationships. We present computational experiments that illustrate the growth and stability of the simulated social networks ensuing from the proposed model. The model and simulation illustrates how network structure influences agent behavior in a social network and how network structures, agent behavior, and agent decisions influence each other.

scholarly journals Graph theoretic models

1980 ◽  
Vol 11 (2) ◽  
pp. 117-121 ◽  
Author(s):  
Frank Harary
Keyword(s):  
Graph Theoretic ◽  
Graph Theoretic Models

Multi-body systems with open chains: Graph-theoretic models

1986 ◽  
Vol 21 (3) ◽  
pp. 273-284 ◽  
Author(s):  
J.C.K Chou ◽  
K Singhal ◽  
H.K Kesavan
Keyword(s):  
Graph Theoretic ◽  
Graph Theoretic Models ◽  
Multi Body

Graph-theoretic models for the Fibonacci family

2014 ◽  
Vol 98 (542) ◽  
pp. 256-265 ◽  
Author(s):  
Thomas Koshy
Keyword(s):  
Fibonacci Numbers ◽  
Mathematical Community ◽  
Large Integer ◽  
Lucas Numbers ◽  
Graph Theoretic ◽  
Fertile Ground ◽  
Graph Theoretic Models ◽  
Fibonacci And Lucas Numbers ◽  
Image Position

The well-known Fibonacci and Lucas numbers continue to faxcinate the mathematical community with their beauty, elegance, ubiquity, and applicability. After several centuries of exploration, they are still a fertile ground for additional activities, for Fibonacci enthusiasts and amateurs alike.Fibonacci numbersFnand Lucas numbersLnbelong to a large integer family {xn}, often defined by the recurrencexn=xn−1+xn−2, wherex1=a,x2=b, andn≥ 3. Whena=b= 1,xn=Fn; and whena= 1 andb= 3,xn=Ln. Clearly,F0= 0 andL0= 2. They satisfy a myriad of elegant properties [1,2,3]. Some of them are:In this article, we will give a brief introduction to theQ-matrix, employ it in the construction of graph-theoretic models [4, 5], and then explore some of these identities using them.In 1960 C.H. King studied theQ-matrix

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