Scale-Free Network Theory in Studying the Structure of the Road Network in Poland

Kocur-Bera, Katarzyna

doi:10.7307/ptt.v26i3.1316

Cited by 10 publications

(8 citation statements)

References 26 publications

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“…How the topology of the larger-scale network of populations affects the propagation of the virus requires simulation studies too extensive to be considered in this paper. For example, whether physical transportation and communication networks are scale-free is controversial [28][29][30]. In our preliminary simulations (not shown), we found that a scale-free random network of 500 villages with populations proportional to the link numbers, and uniform behavior, had a significant probability of entering an endemic state even when the lifetime of immunity was as long as 500 days.…”

Section: Model Features Limitations and Future Studiesmentioning

confidence: 77%

Social Heterogeneity Drives Complex Patterns of the COVID-19 Pandemic: Insights From a Novel Stochastic Heterogeneous Epidemic Model (SHEM)

Maltsev

Stern

2021

Front. Phys.

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In addition to vaccine and impactful treatments, mitigation strategies represent an effective way to combat the COVID-19 virus and an invaluable resource in this task is numerical modeling that can reveal key factors in COVID-19 pandemic development. On the other hand, it has become evident that regional infection curves of COVID-19 exhibit complex patterns which often differ from curves predicted by forecasting models. The wide variations in attack rate observed among different social strata suggest that this may be due to social heterogeneity not accounted for by regional models. We investigated this hypothesis by developing and using a new Stochastic Heterogeneous Epidemic Model that focuses on subpopulations that are vulnerable in the sense of having an increased likelihood of spreading infection among themselves. We found that the isolation or embedding of vulnerable sub-clusters in a major population hub generated complex stochastic infection patterns which included multiple peaks and growth periods, an extended plateau, a prolonged tail, or a delayed second wave of infection. Embedded vulnerable groups became hotspots that drove infection despite efforts of the main population to socially distance, while isolated groups suffered delayed but intense infection. Amplification of infection by these hotspots facilitated transmission from one urban area to another, causing the epidemic to hopscotch in a stochastic manner to places it would not otherwise reach; whereas vaccination only in hotspot populations stopped geographic spread of infection. Our results suggest that social heterogeneity is a key factor in the formation of complex infection propagation patterns. Thus, the mitigation and vaccination of vulnerable groups is essential to control the COVID-19 pandemic worldwide. The design of our new model allows it to be applied in future studies of real-world scenarios on any scale, limited only by computing memory and the ability to determine the underlying topology and parameters.

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Section: Model Features Limitations and Future Studiesmentioning

confidence: 77%

Social Heterogeneity Drives Complex Patterns of the COVID-19 Pandemic: Insights From a Novel Stochastic Heterogeneous Epidemic Model (SHEM)

Maltsev

Stern

2021

Front. Phys.

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“…e preferential attachment can be observed everywhere and can be applied in the urban traffic networks modelling as normally for the representation method of dual approach. Many researches have shown that traffic networks are typically and theoretically scale-free [35,50,51] with dual approach rather than primal approach, with power-law distribution in log-log plot, and the degree distribution exponent has large effects on the performances of traffic network, and the distribution of urban traffic flows also following scale-free properties [52,53]. Zhang [54] proved that 50 extracted dual urban traffic networks of USA are following scale-free properties, and Kalapala et al [55] found that, with the dual representation, the degree distribution of the urban street networks can better fit with the power-law functions with…”

Section: Barabasi-albertmentioning

confidence: 99%

Optimal Topology of Multilayer Urban Traffic Networks

et al. 2019

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Previous urban traffic network-based studies have been based mostly on single-layer networks. Based on their shortcomings, starting from the perspective of a multilayer urban traffic network, this paper takes the different anticongestion abilities and network characteristics of various network structures under the condition of traffic congestion as the research object. Then, a comparative experiment is performed via simulation, and the optimal multilayer urban traffic network topology is obtained under different conditions. It is found that these scale-free related multilayer networks have relatively strong ability to support more traffic flows and have higher anticongestion abilities, regardless of whether it is a lower-layer or upper-layer network. The research results are helpful to deepen our understanding of the characteristics of traffic network structures, help scholars further cognize the structural properties of multilayer urban traffic networks, practically help urban traffic network planners to further optimize the urban traffic network, and broaden the study of multilayer traffic networks.

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“…Many investigations have shown that, in the complex urban system, traffic networks, city-size distributions, distribution of urban traffic flows, and human mobility follow scaling properties [1][2][3][4][5]. Due to a rapid surge of interest since Watts and Strogatz [6], and Barabasi and Albert [7] pointed out the collective dynamics of small-world networks and scalefree networks; complex network theory has become a novel multidisciplinary research direction of complexity science.…”

Section: Introductionmentioning

confidence: 99%

The Complex Network Theory-Based Urban Land-Use and Transport Interaction Studies

Ding

2019

Complexity

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The research on complex networks offers novel insight into the analysis of complex urban systems. The objective of this article is to provide a review of complex network theory in urban land-use and transport studies to date. Some traditional integrated studies of urban land-use and traffic networks are summarized and analysed; related research gaps were proposed. Then, this paper reviewed the application of complex network theory in urban land-use and transport research and practice. It shows that the node importance identification method is critical for network protection or attack studies; the multiple centrality assessment and kernel density estimation approaches can be used to represent the intuitionistic connections of urban traffic networks and surrounding land-uses; it can be used to verify the changing trend and variation of landscape connectivity; also it can be applied to the identification of key changed land-use types in land-use cover change; the coevolution process can be treated as an integrated way to discuss the relationships between urban traffic network growth and land-use change, and the multilayer networks based analysis is a novel method to measure their interactions. This paper is essential in establishing apparent research interests and points out the further potential application of complex network theory in urban traffic network and land-use related studies.

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Scale-Free Network Theory in Studying the Structure of the Road Network in Poland

Abstract: ABSTRACT

Cited by 10 publications

References 26 publications

Social Heterogeneity Drives Complex Patterns of the COVID-19 Pandemic: Insights From a Novel Stochastic Heterogeneous Epidemic Model (SHEM)

Social Heterogeneity Drives Complex Patterns of the COVID-19 Pandemic: Insights From a Novel Stochastic Heterogeneous Epidemic Model (SHEM)

Optimal Topology of Multilayer Urban Traffic Networks

The Complex Network Theory-Based Urban Land-Use and Transport Interaction Studies

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