On the z-counting polynomial for edge-weighted graphs

1992 ◽  
Vol 9 (4) ◽  
pp. 381-387 ◽  
Author(s):  
Sonja Nikolić ◽  
Dejan Plavšić ◽  
Nenad Trinajstić
Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 710
Author(s):  
Modjtaba Ghorbani ◽  
Maryam Jalali-Rad ◽  
Matthias Dehmer

Suppose ai indicates the number of orbits of size i in graph G. A new counting polynomial, namely an orbit polynomial, is defined as OG(x) = ∑i aixi. Its modified version is obtained by subtracting the orbit polynomial from 1. In the present paper, we studied the conditions under which an integer polynomial can arise as an orbit polynomial of a graph. Additionally, we surveyed graphs with a small number of orbits and characterized several classes of graphs with respect to their orbit polynomials.


2000 ◽  
Vol 32 (4) ◽  
pp. 477-483 ◽  
Author(s):  
Bernd Metzger ◽  
Peter Stollmann

2015 ◽  
Vol 219 (9) ◽  
pp. 3889-3912 ◽  
Author(s):  
Bethany Kubik ◽  
Sean Sather-Wagstaff
Keyword(s):  

Author(s):  
Ronald Manríquez ◽  
Camilo Guerrero-Nancuante ◽  
Felipe Martínez ◽  
Carla Taramasco

The understanding of infectious diseases is a priority in the field of public health. This has generated the inclusion of several disciplines and tools that allow for analyzing the dissemination of infectious diseases. The aim of this manuscript is to model the spreading of a disease in a population that is registered in a database. From this database, we obtain an edge-weighted graph. The spreading was modeled with the classic SIR model. The model proposed with edge-weighted graph allows for identifying the most important variables in the dissemination of epidemics. Moreover, a deterministic approximation is provided. With database COVID-19 from a city in Chile, we analyzed our model with relationship variables between people. We obtained a graph with 3866 vertices and 6,841,470 edges. We fitted the curve of the real data and we have done some simulations on the obtained graph. Our model is adjusted to the spread of the disease. The model proposed with edge-weighted graph allows for identifying the most important variables in the dissemination of epidemics, in this case with real data of COVID-19. This valuable information allows us to also include/understand the networks of dissemination of epidemics diseases as well as the implementation of preventive measures of public health. These findings are important in COVID-19’s pandemic context.


2018 ◽  
Vol 47 (1) ◽  
pp. 67-84 ◽  
Author(s):  
Jiyou Li ◽  
Daqing Wan

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