Dynamic evolution of shipping network based on hypergraph

Yu, Ping; Wang, Zhiping; Yin, Haofei; Wang, Jia; Xu, Zhaohui

doi:10.1016/j.physa.2022.127247

Cited by 7 publications

(3 citation statements)

References 18 publications

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“…When the exponent α = 1, our model degrades to that of refs. [25][26][27][28] to some extent, the shifted power law distributions are all obtained. When the exponent α is not equal to 1, the hyper-degree distribution in the form of Weibull function is first deduced.…”

mentioning

confidence: 77%

“…Meanwhile, the hyper-network models presented in this paper are not only suitable for the descriptions of civil aviation systems of other countries and continents, but also can depict the shipping networks [26], railway networks [46], since the constructions of infrastructures for them are also constrained by resources at a first glance. Besides that, the duplication of nodes between hyperedges of our second model might be used to explain the non-power law properties of cardinalities for the scientific collaboration systems [23,39], because this mechanism could roughly mirror the behaviour for which the author of a paper contributes to other papers with different authors, when the authors/papers are regarded as nodes/hyper-edges.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

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Hyper-network models for civil aviation systems

Sun,

He,

Zhang

et al. 2024

EPL

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In a civil aviation system, each air-route could penetrate several sectors, and each sector can also be passed through by multiple air-routes. Two dual hyper-networks are proposed to model the facts, where the air-routes/sectors are taken as nodes/hyper-edges (model RS) and hyper-edges/nodes (model SR), respectively. They both share a growth mechanism of preferential attachment, resulting from which the Weibull distributions for the hyper-degrees of model RS and the hyper-edge cardinalities of model SR. The civil aviation systems in Mainland China turn out to follow an anti-preferential attachment.

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mentioning

confidence: 77%

Section: Conclusion and Discussionmentioning

confidence: 99%

Hyper-network models for civil aviation systems

Sun,

He,

Zhang

et al. 2024

EPL

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“…Research on propagation has focused on the study of network models and the study of propagation mechanisms. Network models include dynamic networks [5][6][7], scale-free networks [8,9], node-weighted networks [10], edge-weighted networks [11], variable-growth networks [12], and correlation networks [13]. The study of propagation mechanisms includes SIS [14], SIR [15], etc.…”

Section: Introductionmentioning

confidence: 99%

Study of Information Dissemination in Hypernetworks with Adjustable Clustering Coefficient

Wei

Ding

et al. 2023

Applied Sciences

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The structure of a model has an important impact on information dissemination. Many information models of hypernetworks have been proposed in recent years, in which nodes and hyperedges represent the individuals and the relationships between the individuals, respectively. However, these models select old nodes based on preference attachment and ignore the effect of aggregation. In real life, friends of friends are more likely to form friendships with each other, and a social network should be a hypernetwork with an aggregation phenomenon. Therefore, a social hypernetwork evolution model with adjustable clustering coefficients is proposed. Subsequently, we use the SIS (susceptible–infectious–susceptible) model to describe the information propagation process in the aggregation-phenomenon hypernetwork. In addition, we establish the relationship between the density of informed nodes and the structural parameters of the hypernetwork in a steady state using the mean field theory. Notably, modifications to the clustering coefficients do not impact the hyperdegree distribution; however, an increase in the clustering coefficients results in a reduced speed of information dissemination. It is further observed that the model can degenerate to a BA (Barabási–Albert) hypernetwork by setting the clustering coefficient to zero. Thus, the aggregation-phenomenon hypernetwork is an extension of the BA hypernetwork with stronger applicability.

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