Robustness of multilayer networks: A graph energy perspective

Kumar, Rajesh; Kumari, Suchi; Mishra, Anubhav

doi:10.1016/j.physa.2023.129160

Cited by 4 publications

(1 citation statement)

References 21 publications

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“…This metric emphasizes the network's efficiency in maintaining 'good paths' even after attacks. Recent research by Kumar et al [25] proposed a new method of robustness evaluation to overcome the limitations of LCC-based analysis. They introduced various energy and efficiency parameters, along with edge weights based on node reliability, to evaluate the robustness of multi-layer networks.…”

Section: Robustness Analysismentioning

confidence: 99%

Robustness Analysis of Public Transportation Systems in Seoul Based on General Multi-Layer Networks

Lee,

Kim,

Kim

2024

Preprint

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Public transportation systems play a vital role in modern cities, enhancing the quality of life and fostering sustainable economic growth. Modeling and understanding the complexities of these transportation networks are crucial for effective urban planning and management. Traditional models often fall short in capturing the intricate interactions and interdependencies in multi-modal public transportation systems. To address this challenge, recent research has embraced multi-layer network models, offering a more sophisticated representation of these networks. However, there is a need to explore and develop robustness analysis techniques tailored to these general multi-layer networks to fully assess their complexities in real-world scenarios. In this paper, we employ a general multi-layer network model to comprehensively analyze a real-world multi-modal transportation network in Seoul, South Korea. We leverage a large volume of traffic data to model, visualize, and evaluate the city's mobility patterns. Additionally, we introduce two novel methodologies for robustness analysis, one based on random walk coverage and the other on Eigenvalue, specifically designed for general multi-layer networks. Extensive experiments using the large volume of real-world data sets demonstrate the effectiveness of the proposed approaches.

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Section: Robustness Analysismentioning

confidence: 99%

Robustness Analysis of Public Transportation Systems in Seoul Based on General Multi-Layer Networks

Lee,

Kim,

Kim

2024

Preprint

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Robustness Analysis of Public Transportation Systems in Seoul Using General Multilayer Network Models

Lee,

Kim,

Kim

2024

J Supercomput

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Optimizing network insights: AI-Driven approaches to circulant graph based on Laplacian spectra

Raza,

Munir,

Hussain

2024

Phys. Scr.

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Exploring the applications of Laplacian and signless Laplacian spectra extends beyond theoretical chemistry, computer science, electrical networks, and complex networks. These spectra, with their relevance, provide valuable insights into the structures of real-world networks and facilitate the prediction of their structural properties. The focal point of the study lies in the spectrum-based analysis of circulant graphs. From these analyses, crucial network measures such as mean-ﬁrst passage time, average path length, spanning trees, and spectral radius are derived. This research enriches our comprehension of the interplay between graph spectra and network characteristics, oﬀering a holistic understanding of complex networks. Consequently, it contributes to the ability to make predictions and conduct analyses across diverse scientiﬁc disciplines.

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Robustness of multilayer networks: A graph energy perspective

Cited by 4 publications

References 21 publications

Robustness Analysis of Public Transportation Systems in Seoul Based on General Multi-Layer Networks

Robustness Analysis of Public Transportation Systems in Seoul Based on General Multi-Layer Networks

Robustness Analysis of Public Transportation Systems in Seoul Using General Multilayer Network Models

Optimizing network insights: AI-Driven approaches to circulant graph based on Laplacian spectra

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