Transient Dynamics of Epidemic Spreading and Its Mitigation on Large Networks

Lee, Chul‐Ho; Tenneti, Srinivas; Eun, Do Young

doi:10.1145/3323679.3326517

Cited by 21 publications

(19 citation statements)

References 54 publications

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“…The purpose of this new metric is precisely to take into consideration long-range interactions between institutions. Some important similarities can be found between this new metric and the resistance distance, a well-known metric in network theory derived from the study of electric circuits (Klein and Randic 1993;Estrada and Hatano 2010a;Lee et al 2019), and its interpretation in terms of vibrational communicability (Estrada and Hatano 2010b;Bozzo 2013;Ferraz de Arruda et al 2014;Van Mieghem et al 2017).…”

Section: Literature Reviewmentioning

confidence: 80%

Community structure in the World Trade Network based on communicability distances

Bartesaghi

Clemente

Grassi

2020

J Econ Interact Coord

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In this paper, we investigate the mesoscale structure of the World Trade Network. In this framework, a specific role is assumed by short- and long-range interactions, and hence by any suitably defined network-based distance between countries. Therefore, we identify clusters through a new procedure that exploits Estrada communicability distance and the vibrational communicability distance, which turn out to be particularly suitable for catching the inner structure of the economic network. The proposed methodology aims at finding the distance threshold that maximizes a specific quality function defined for general metric spaces. Main advantages regard the computational efficiency of the procedure as well as the possibility to inspect intercluster and intracluster properties of the resulting communities. The numerical analysis highlights peculiar relationships between countries and provides a rich set of information that can hardly be achieved within alternative clustering approaches.

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Section: Literature Reviewmentioning

confidence: 80%

Community structure in the World Trade Network based on communicability distances

Bartesaghi

Clemente

Grassi

2020

J Econ Interact Coord

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“…To sort out this problem we will follow here Lee, Tenneti, and Eun (2019), who proposed the following change of variable to avoid the aforementioned problems with the solution of the linearized SI model:…”

Section: Susceptible-infected Modelmentioning

confidence: 99%

Communicability distance reveals hidden patterns of Alzheimer’s disease

Lella

Estrada

2020

Network Neuroscience

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The communicability distance between pairs of regions in human brain is used as a quantitative proxy for studying Alzheimer’s disease. Using this distance, we obtain the shortest communicability path lengths between different regions of brain networks from patients with Alzheimer’s disease (AD) and healthy cohorts (HC). We show that the shortest communicability path length is significantly better than the shortest topological path length in distinguishing AD patients from HC. Based on this approach, we identify 399 pairs of brain regions for which there are very significant changes in the shortest communicability path length after AD appears. We find that 42% of these regions interconnect both brain hemispheres, 28% connect regions inside the left hemisphere only, and 20% affect vermis connection with brain hemispheres. These findings clearly agree with the disconnection syndrome hypothesis of AD. Finally, we show that in 76.9% of damaged brain regions the shortest communicability path length drops in AD in relation to HC. This counterintuitive finding indicates that AD transforms the brain network into a more efficient system from the perspective of the transmission of the disease, because it drops the circulability of the disease factor around the brain regions in relation to its transmissibility to other regions.

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“…To sort out this problem we will follow here Lee et al [42], who proposed the following change of variable to avoid the aforementioned problems with the solution of the linearized SI model:…”

Section: Susceptible-infected Modelmentioning

confidence: 99%

Communicability distance reveals hidden patterns of Alzheimer disease

Lella

Estrada

2020

Preprint

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AbstractThe communicability distance between pairs of regions in human brain is used as a quantitative proxy for studying Alzheimer disease. Using this distance we obtain the shortest communicability path lengths between different regions of brain networks from Alzheimer diseased (AD) patients and healthy cohorts (HC). We show that the shortest communicability path length is significantly better than the shortest topological path length in distinguishing AD patients from HC. Based on this approach we identify 399 pairs of brain regions for which there are very significant changes in the shortest communicability path length after AD appears. We find that 42% of these regions interconnect both brain hemispheres, 28% connect regions inside the left hemisphere only and 20% affects vermis connection with brain hemispheres. These findings clearly agree with the disconnection syndrome hypothesis of Alzheimer disease. Finally, we show that in 76.9% damaged brain regions the shortest communicability path length drops in AD in relation to HC. This counterintuitive finding indicates that AD transforms the brain network into a more efficient system from the perspective of the transmission of the disease, because it drops the circulability of the disease factor around the brain regions in relation to its transmissibility to other regions.

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Transient Dynamics of Epidemic Spreading and Its Mitigation on Large Networks

Cited by 21 publications

References 54 publications

Community structure in the World Trade Network based on communicability distances

Community structure in the World Trade Network based on communicability distances

Communicability distance reveals hidden patterns of Alzheimer’s disease

Communicability distance reveals hidden patterns of Alzheimer disease

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