Normalised degree variance

Smith, Keith; Escudero, Javier

doi:10.1007/s41109-020-00273-3

Cited by 11 publications

(8 citation statements)

References 33 publications

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“…A network analysis was also performed on the relationships between trends and industries. Fundamental parameters of a network are its number of nodes, otherwise known as the network size, and the number of edges [ 43 ]. We are surrounded by naturally connected structures and networks [ 44 ].…”

Section: Findings and Discussionmentioning

confidence: 99%

Social media analytics of the Internet of Things

Scheibmeir

Malaiya

2021

Discov Internet Things

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The Internet of Things technology offers convenience and innovation in areas such as smart homes and smart cities. Internet of Things solutions require careful management of devices and the risk mitigation of potential vulnerabilities within cyber-physical systems. The Internet of Things concept, its implementations, and applications are frequently discussed on social media platforms. This research illuminates the public view of the Internet of Things through a content-based and network analysis of contemporary conversations occurring on the Twitter platform. Tweets can be analyzed with machine learning methods to converge the volume and variety of conversations into predictive and descriptive models. We have reviewed 684,503 tweets collected in a 2-week period. Using supervised and unsupervised machine learning methods, we have identified trends within the realm of IoT and their interconnecting relationships between the most mentioned industries. We have identified characteristics of language sentiment which can help to predict the popularity of IoT conversation topics. We found the healthcare industry as the leading use case industry for IoT implementations. This is not surprising as the current COVID-19 pandemic is driving significant social media discussions. There was an alarming dearth of conversations towards cybersecurity. Recent breaches and ransomware events denote that organizations should spend more time communicating about risks and mitigations. Only 12% of the tweets relating to the Internet of Things contained any mention of topics such as encryption, vulnerabilities, or risk, among other cybersecurity-related terms. We propose an IoT Cybersecurity Communication Scorecard to help organizations benchmark the density and sentiment of their corporate communications regarding security against their specific industry.

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Section: Findings and Discussionmentioning

confidence: 99%

Social media analytics of the Internet of Things

Scheibmeir

Malaiya

2021

Discov Internet Things

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“…This indicates how quickly on average one can get from one node to any other in the network. Normalised degree variance 35 , V . This is a normalised measure of the variance of the degree distribution of the network, where is the degree of node i , is the average degree of the network and P is the network density.…”

Section: Methodsmentioning

confidence: 99%

Explaining the emergence of complex networks through log-normal fitness in a Euclidean node similarity space

Smith

2021

Sci Rep

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Networks of disparate phenomena—be it the global ecology, human social institutions, within the human brain, or in micro-scale protein interactions—exhibit broadly consistent architectural features. To explain this, we propose a new theory where link probability is modelled by a log-normal node fitness (surface) factor and a latent Euclidean space-embedded node similarity (depth) factor. Building on recurring trends in the literature, the theory asserts that links arise due to individualistic as well as dyadic information and that important dyadic information making up the so-called depth factor is obscured by this essentially non-dyadic information making up the surface factor. Modelling based on this theory considerably outperforms popular power-law fitness and hyperbolic geometry explanations across 110 networks. Importantly, the degree distributions of the model resemble power-laws at small densities and log-normal distributions at larger densities, posing a reconciliatory solution to the long-standing debate on the nature and existence of scale-free networks. Validating this theory, a surface factor inversion approach on an economic world city network and an fMRI connectome results in considerably more geometrically aligned nearest neighbour networks, as is hypothesised to be the case for the depth factor. This establishes new foundations from which to understand, analyse, deconstruct and interpret network phenomena.

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“…Several definitions of network heterogeneity, based on the distribution of the node degree centrality, have been proposed. Snijders (27), Zimmermann et al (28), and Smith and Escudero (29) proposed to use a nonstandardized or normalized definition of heterogeneity based on the average degree, the variance of the quasistar network, or by the number of graph-connected components, respectively. Similarly, Von Collatz et al (30) quantified heterogeneity by comparing the largest eigenvalue of the adjacency matrix with the average degree.…”

Section: Network Heterogeneitymentioning

confidence: 99%

Approach to Quantify the Impact of Disruptions on Traffic Conditions using Dynamic Weighted Resilience Metrics of Transport Networks

Henry

Furno

Faouzi

2021

Transportation Research Record

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Transport networks are essential for societies. Their proper operation has to be preserved to face any perturbation or disruption. It is therefore of paramount importance that the modeling and quantification of the resilience of such networks are addressed to ensure an acceptable level of service even in the presence of disruptions. The paper aims at characterizing network resilience through weighted degree centrality. To do so, a real dataset issued from probe vehicle data is used to weight the graph by the traffic load. In particular, a set of disrupted situations retrieved from the study dataset is analyzed to quantify the impact on network operations. Results demonstrate the ability of the proposed metrics to capture traffic dynamics as well as their utility for quantifying the resilience of the network. The proposed methodology combines different metrics from the complex networks theory (i.e., heterogeneity, density, and symmetry) computed on temporal and weighted graphs. Time-varying traffic conditions and disruptions are analyzed by providing relevant insights on the network states via three-dimensional maps.

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Normalised degree variance

Cited by 11 publications

References 33 publications

Social media analytics of the Internet of Things

Social media analytics of the Internet of Things

Explaining the emergence of complex networks through log-normal fitness in a Euclidean node similarity space

Approach to Quantify the Impact of Disruptions on Traffic Conditions using Dynamic Weighted Resilience Metrics of Transport Networks

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