Topological Analysis of Synthetic Models for Air Transportation Multilayer Networks

Fügenschuh, Marzena; Gera, Ralucca; Tagarelli, Andrea

doi:10.1007/978-3-030-65351-4_17

Cited by 2 publications

(1 citation statement)

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“…Every single layer represents distinct meaningful information from its own perspective. For example, in a case of critical infrastructures the first layer may corresponds to a power system, the second layer may represent a transportation system, while the third layer may be a telecommunication sector, and most importantly, all these layers are also tightly interconnected (Fügenschuh et al, 2021; Yang et al, 2021). Hence, we now deal with community detection not in a single network but in a network of networks , requiring to exploit and fuse multiple aspects of disparate, yet interdependent sources of information.…”

Section: Community Detection In Multilayer Multiscale and Hypergraph ...mentioning

confidence: 99%

Community detection in complex networks: From statistical foundations to data science applications

Dey

Tian

Gel

2021

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Identifying and tracking community structures in complex networks are one of the cornerstones of network studies, spanning multiple disciplines, from statistics to machine learning to social sciences, and involving even a broader range of application areas, from biology to politics to blockchain. This survey paper aims to provide an overview of some most popular approaches in statistical network community detection as well as the newly emerging research directions such as community extraction with higher‐order features and community discovery in multilayer and multiscale networks. Our goal is to offer a unified view at methodological interconnections and the wide spectrum of interdisciplinary data science applications of network community analysis. This article is categorized under: Data: Types and Structure > Graph and Network Data Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification

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Section: Community Detection In Multilayer Multiscale and Hypergraph ...mentioning

confidence: 99%

Community detection in complex networks: From statistical foundations to data science applications

Dey

Tian

Gel

2021

WIREs Computational Stats

View full text Add to dashboard Cite

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Structural and spectral properties of generative models for synthetic multilayer air transportation networks

et al. 2021

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To understand airline transportation networks (ATN) systems we can effectively represent them as multilayer networks, where layers capture different airline companies, the nodes correspond to the airports and the edges to the routes between the airports. We focus our study on the importance of leveraging synthetic generative multilayer models to support the analysis of meaningful patterns in these routes, capturing an ATN’s evolution with an emphasis on measuring its resilience to random or targeted attacks and considering deliberate locations of airports. By resorting to the European ATN and the United States ATN as exemplary references, in this work, we provide a systematic analysis of major existing synthetic generation models for ATNs, specifically ANGEL, STARGEN and BINBALL. Besides a thorough study of the topological aspects of the ATNs created by the three models, our major contribution lays on an unprecedented investigation of their spectral characteristics based on Random Matrix Theory and on their resilience analysis based on both site and bond percolation approaches. Results have shown that ANGEL outperforms STARGEN and BINBALL to better capture the complexity of real-world ATNs by featuring the unique properties of building a multiplex ATN layer by layer and of replicating layers with point-to-point structures alongside hub-spoke formations.

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Topological Analysis of Synthetic Models for Air Transportation Multilayer Networks

Cited by 2 publications

References 20 publications

Community detection in complex networks: From statistical foundations to data science applications

Community detection in complex networks: From statistical foundations to data science applications

Structural and spectral properties of generative models for synthetic multilayer air transportation networks

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