The graph and network theory as a tool to model and simulate the dynamics of infectious diseases

Bartosiak, C.; Kasprzyk, Rafał; Najgebauer, Andrzej

doi:10.1515/bams-2013-0003

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…In graph theory and network science, centrality is used to identify the most important nodes within a graph structure. For example, in social networks, the central nodes identify the most influential person in a community, or in analysing the spread of a disease, the central nodes determine the superspreader [13]. The centrality measure in a graph is a real value function on the nodes of the graph which determines the rank of the importance of each node.…”

Section: Estimation Of Number Of Graph Clustersmentioning

confidence: 99%

Centrality Based Number of Cluster Estimation in Graph Clustering

Shamsi

Beheshti

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Graph clustering algorithms require the number of clusters as an input. However, in many real-world practical applications, the correct number of clusters is unknown. Determining the optimal number of clusters for graph clustering algorithms is an essential and challenging task, which is a form of model order selection. Here, we propose a new algorithm for estimating the number of clusters in a graph using the centrality measure. In graph theory, the centrality measure is used for determining the most important and most influential nodes within a graph. The proposed centrality based number of cluster estimation (CB-NCE) method considers minimizing the probabilistic bounds on the average central error of centrality. The desired criterion represents an information theoretic distance measure in the form of description length of centrality. The simulation results show the superior performance of the proposed algorithm among other existing methods, in terms of clustering performance metrics such as normalized mutual information, Rand index, and F-measure.

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Section: Estimation Of Number Of Graph Clustersmentioning

confidence: 99%

Centrality Based Number of Cluster Estimation in Graph Clustering

Shamsi

Beheshti

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Our knowledge about most real networks is incomplete and uncertain; that's why the target strategy is very often unusable. Then, as experiments prove, the random-random strategy could be utilized, which is much more effective than the random one (Bartosiak at. al.…”

Section: Effectiveness Of Removal Strategies In Networkmentioning

confidence: 99%

“…This experiment has been repeated 100 times. The dynamics of an infectious disease in different networks is presented in Figure 20 (Bartosiak at. al 2013;Kasprzyk 2012a).…”

Section: Effectiveness Of Removal Strategies In Networkmentioning

confidence: 99%

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Experimental Environment to Model, Simulate and Analyze Contagious Diseases as a Diffusion Process in Social Networks

Kasprzyk

Najgebauer

2021

Preprint

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In this paper the novel model of diffusion on networks and the experimental environment are presented. We consider the utilization of the graph and network theory in the field of modelling and simulating the dynamics of contagious diseases. We describe basic principles and methods and show how we can use them to fight against the spread of this phenomenon. We also present our software solution – CARE (Creative Application to Remedy Epidemics) that can be used to support decision-making activities.

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Modeling and simulation of botnet based cyber-threats

2017

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Abstract. The paper presents an analysis of cyber-threats, with particular emphasis on the threats resulting from botnet activity. Botnets are the most common types of threats and often perceived as crucial in terms of national security. Their classification and methods of spreading are the basis for creating cyberspace model including the presence of different types of cyber-threats. A well-designed cyberspace model enables to construct an experimental environment that allows for the analysis of botnet characteristics, testing its resistance to various events and simulation of the spread and evolution. For this purpose, dedicated platforms with capabilities and functional characteristics to meet these requirements have been proposed.

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The graph and network theory as a tool to model and simulate the dynamics of infectious diseases

Cited by 5 publications

References 10 publications

Centrality Based Number of Cluster Estimation in Graph Clustering

Centrality Based Number of Cluster Estimation in Graph Clustering

Experimental Environment to Model, Simulate and Analyze Contagious Diseases as a Diffusion Process in Social Networks

Modeling and simulation of botnet based cyber-threats

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