ANGEL: A Synthetic Model for Airline Network Generation Emphasizing Layers

Fügenschuh, Marzena; Gera, Ralucca; Tagarelli, Andrea

doi:10.1109/tnse.2020.2965207

Cited by 5 publications

(9 citation statements)

References 35 publications

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“…The hub-spoke layers produced by both BINBALL and STARGEN result in homogeneous structures due to the way a preferential attachment method is applied. However, as found in [ 45 ] based on a thorough investigation of the EATN network, most layers appear to show a mixture of both hub-spoke and point-to-point structures. Addressing this crucial aspect is a major focus in the ANGEL model, the algorithmic scheme of which is sketched in Algorithm 2.…”

Section: Introductionmentioning

confidence: 53%

“…A further extension is introduced by the STARGEN model that extends the preferential attachment of BINBALL model by enforcing both a differentiated layer set sizes as well as a hub-spoke layer model observed in the early studies at ATNs [ 44 ]. Moreover, a different approach is defined in the ANGEL model, which removes attention from the preferential attachment to gain more influence on the intra- and inter-layer structure of the multilayer synthetic network created [ 45 ]. A preliminary analysis and comparison of these synthetic models followed recently [ 46 ], which we use as inspiration for this work.…”

Section: Introductionmentioning

confidence: 99%

“…The multiplex finally emerges as a multigraph obtained as the union of all nodes, discounting repetition, and all the edges in the layers, allowing the repetition from different layers. We refer the interested reader to [ 45 ] for further details.…”

Section: Introductionmentioning

confidence: 99%

“…The European ATN (EATN) was originally studied in [ 49 ] and extensively analyzed in our earlier work [ 45 ]. We refer the interested reader to the above works for further details, whereas here we provide an overview through a selection of statistics reported in Table 1 .…”

Section: Introductionmentioning

confidence: 99%

“…The affinity of a graph G (with edge-set E ) to build hub-spoke formations can be measured using the s-metric value, [ 50 ]. In [ 45 ], the s-metric formula is applied to set up an empirical definition of a hub. According to it, a node is identified as a hub if where V L and E L denote the set of nodes and edges in L , respectively.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 53%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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Topological Analysis of Synthetic Models for Air Transportation Multilayer Networks

Fügenschuh

Gera

Tagarelli

2021

Studies in Computational Intelligence

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TenGAN: adversarially generating multiplex tensor graphs

Shiao,

Miller,

Chan

et al. 2023

Data Min Knowl Disc

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In this work, we explore multiplex graph (networks with different types of edges) generation with deep generative models. We discuss some of the challenges associated with multiplex graph generation that make it a more difficult problem than traditional graph generation. We propose TenGAN, the first neural network for multiplex graph generation, which greatly reduces the number of parameters required for multiplex graph generation. We also propose 3 different criteria for evaluating the quality of generated graphs: a graph-attribute-based, a classifier-based, and a tensor-based method. We evaluate its performance on 4 datasets and show that it generally performs better than other existing statistical multiplex graph generative models. We also adapt HGEN, an existing deep generative model for heterogeneous information networks, to work for multiplex graphs and show that our method generally performs better.

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ANGEL: A Synthetic Model for Airline Network Generation Emphasizing Layers

Cited by 5 publications

References 35 publications

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

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

Topological Analysis of Synthetic Models for Air Transportation Multilayer Networks

TenGAN: adversarially generating multiplex tensor graphs

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