About: Infection dynamics on spatial small-world network models

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Infection dynamics on spatial small-world network models
Creator	Antonioni, Alberto Bullock, Seth Darabos, Christian Giacobini, Mario Iotti, Bryan Tomassini, Marco
Source	PMC
abstract	The study of complex networks, and in particular of social networks, has mostly concentrated on relational networks, abstracting the distance between nodes. Spatial networks are, however, extremely relevant in our daily lives, and a large body of research exists to show that the distances between nodes greatly influence the cost and probability of establishing and maintaining a link. A random geometric graph (RGG) is the main type of synthetic network model used to mimic the statistical properties and behavior of many social networks. We propose a model, called REDS, that extends energy-constrained RGGs to account for the synergic effect of sharing the cost of a link with our neighbors, as is observed in real relational networks. We apply both the standard Watts-Strogatz rewiring procedure and another method that conserves the degree distribution of the network. The second technique was developed to eliminate unwanted forms of spatial correlation between the degree of nodes that are affected by rewiring, limiting the effect on other properties such as clustering and assortativity. We analyze both the statistical properties of these two network types and their epidemiological behavior when used as a substrate for a standard susceptible-infected-susceptible compartmental model. We consider and discuss the differences in properties and behavior between RGGs and REDS as rewiring increases and as infection parameters are changed. We report considerable differences both between the network types and, in the case of REDS, between the two rewiring schemes. We conclude that REDS represent, with the application of these rewiring mechanisms, extremely useful and interesting tools in the study of social and epidemiological phenomena in synthetic complex networks.
has issue date	2017-11-30(xsd:dateTime)
bibo:doi	10.1103/physreve.96.052316
bibo:pmid	29347688
has license	no-cc
sha1sum (hex)	4ba44e8b11efcaa96ef3f04364e6dd623e839768
schema:url	https://doi.org/10.1103/physreve.96.052316
resource representing a document's title	Infection dynamics on spatial small-world network models
has PubMed Central identifier	PMC7217528
has PubMed identifier	29347688
schema:publication	Phys Rev E
resource representing a document's body	covid:4ba44e8b11efcaa96ef3f04364e6dd623e839768#body_text
is schema:about of	named entity 'concentrated' named entity 'real' named entity 'distances' named entity 'substrate' named entity 'networks' named entity 'Infection' named entity 'small-world network' named entity 'TYPES' named entity 'CONSIDERABLE' named entity 'eliminate' named entity 'infection' named entity 'abstracting' named entity 'represent' named entity 'tools' named entity 'large' named entity 'nodes' named entity 'complex networks' named entity 'complex networks' named entity 'standard' named entity 'mechanisms' named entity 'extremely' named entity 'behavior' named entity 'developed' named entity 'probability' named entity 'infection' named entity 'epidemiological' named entity 'contour lines' named entity 'random geometric graphs' named entity 'infectious diseases' named entity 'continuum percolation' named entity 'small worlds' named entity 'network structure' named entity 'diffusion' named entity 'network model' named entity 'Euclidean length' named entity 'assortativity' named entity 'social network' named entity 'social interactions' named entity 'topological properties' named entity 'developing country' named entity 'random network model' named entity 'small-world effect' named entity 'complex network' named entity 'network structure' named entity '0.01' named entity 'degree distribution' named entity 'probability' named entity 'small-world' named entity 'epidemiological' named entity 'network structure' named entity '1, 2' named entity 'uniformly at random' named entity 'small-world' named entity 'Facebook' named entity 'degree distribution' named entity 'spatial network' named entity 'network topology' named entity 'metric space' named entity 'probability' named entity 'degree distribution' named entity 'community structure' named entity 'small-world effect' named entity 'degree distribution' named entity 'small-world' named entity 'assortativity' named entity 'spatial network' named entity 'epidemiological' named entity 'uniformly at random' named entity 'assortativity' named entity 'compartmental model' named entity 'complex networks' named entity 'probability' named entity 'probability' named entity 'Watts and Strogatz' named entity 'epidemiology' named entity 'community structure'

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