Edge-based stochastic network model reveals structural complexity of edges

2019 ◽  
Vol 100 ◽  
pp. 1073-1087 ◽  
Author(s):  
Xuemeng Zhai ◽  
Wanlei Zhou ◽  
Gaolei Fei ◽  
Cai Lu ◽  
Sheng Wen ◽  
...  
Keyword(s):  
Network Model ◽  
Structural Complexity ◽  
Stochastic Network ◽  
Edge Based

Branching probabilities planning of stochastic network model using genetic algorithm supported by sensitivity analysis

2007 ◽  
Vol 162 (4) ◽  
pp. 43-53
Author(s):  
Kenzo Kurihara ◽  
Manabu Nagai ◽  
Nobuyuki Nishiuchi ◽  
Kazuaki Masuda
Keyword(s):  
Genetic Algorithm ◽  
Sensitivity Analysis ◽  
Network Model ◽  
Stochastic Network

scholarly journals Logistics of community smallpox control through contact tracing and ring vaccination: a stochastic network model

2004 ◽  
Vol 4 (1) ◽  
Author(s):  
Travis C Porco ◽  
Karen A Holbrook ◽  
Susan E Fernyak ◽  
Diane L Portnoy ◽  
Randy Reiter ◽  
...  
Keyword(s):  
Network Model ◽  
Contact Tracing ◽  
Stochastic Network ◽  
Ring Vaccination

scholarly journals Network entropy using edge-based information functionals

2020 ◽  
Vol 8 (3) ◽  
Author(s):  
Furqan Aziz ◽  
Edwin R Hancock ◽  
Richard C Wilson
Keyword(s):  
Diffusion Process ◽  
Complex Networks ◽  
Structural Information ◽  
Structural Complexity ◽  
Network Models ◽  
Quantum Graph ◽  
Heat Diffusion ◽  
Graph Representation ◽  
Edge Based ◽  
Local Edge

Abstract In this article, we present a novel approach to analyse the structure of complex networks represented by a quantum graph. A quantum graph is a metric graph with a differential operator (including the edge-based Laplacian) acting on functions defined on the edges of the graph. Every edge of the graph has a length interval assigned to it. The structural information contents are measured using graph entropy which has been proved useful to analyse and compare the structure of complex networks. Our definition of graph entropy is based on local edge functionals. These edge functionals are obtained by a diffusion process defined using the edge-based Laplacian of the graph using the quantum graph representation. We first present the general framework to define graph entropy using heat diffusion process and discuss some of its properties for different types of network models. Second, we propose a novel signature to gauge the structural complexity of the network and apply the proposed method to different datasets.

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