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

Abstract

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.

Reconstructing Sparse Multiplex Networks with Application to Covert Networks

“…As such, it is valuable to compare the reconstruction accuracy of EMA and GNN on multiple datasets and identify where each approach performs the best. Furthermore, since the accuracy of the EMA framework reduces for multiplex networks with many layers, future research can explore how to improve the predictive accuracy using network generative models with a prescribed level of interlayer dependency [57][58][59][60], and use the dynamics of the networks [61].…”