Dynamic Network of United States Air Transportation at Multiple Levels

Charyyev, Batyr; Solmaz, Mustafa; Güneş, Mehmet Hadi

doi:10.1007/978-3-030-40943-2_24

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…Thus, the methodology could be applied to one of such network models to identify the most adequate backbone extraction method. Such method could then be used to extract different backbones (one for each window) allowing an assessment of the temporal evolution of their properties (as done in [21,22,25,55,69,70,76,86]).…”

Section: Step 1-building a Network Modelmentioning

confidence: 99%

On network backbone extraction for modeling online collective behavior

et al. 2022

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Collective user behavior in social media applications often drives several important online and offline phenomena linked to the spread of opinions and information. Several studies have focused on the analysis of such phenomena using networks to model user interactions, represented by edges. However, only a fraction of edges contribute to the actual investigation. Even worse, the often large number of non-relevant edges may obfuscate the salient interactions, blurring the underlying structures and user communities that capture the collective behavior patterns driving the target phenomenon. To solve this issue, researchers have proposed several network backbone extraction techniques to obtain a reduced and representative version of the network that better explains the phenomenon of interest. Each technique has its specific assumptions and procedure to extract the backbone. However, the literature lacks a clear methodology to highlight such assumptions, discuss how they affect the choice of a method and offer validation strategies in scenarios where no ground truth exists. In this work, we fill this gap by proposing a principled methodology for comparing and selecting the most appropriate backbone extraction method given a phenomenon of interest. We characterize ten state-of-the-art techniques in terms of their assumptions, requirements, and other aspects that one must consider to apply them in practice. We present four steps to apply, evaluate and select the best method(s) to a given target phenomenon. We validate our approach using two case studies with different requirements: online discussions on Instagram and coordinated behavior in WhatsApp groups. We show that each method can produce very different backbones, underlying that the choice of an adequate method is of utmost importance to reveal valuable knowledge about the particular phenomenon under investigation.

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Section: Step 1-building a Network Modelmentioning

confidence: 99%

On network backbone extraction for modeling online collective behavior

et al. 2022

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“…Backbone extraction has a wide range of applications. One uses it for various types of networks, such as social [61][62][63][64][65][66][67][68][69] , biological 70 , brain [71][72][73][74] , gene [75][76][77][78][79] , metabolic [80][81][82][83][84] , food web 85,86 , environmental 87,88 , finance [89][90][91][92] , trade [93][94][95][96][97] , information [98][99][100][101] , political 102,103 , transportation [104][105][106][107][108] , and others [109][110]…”

Section: Introductionmentioning

confidence: 99%

An Evaluation Tool for Backbone Extraction Techniques in Weighted Complex Networks

Yassin

Haidar

Cherifi

et al. 2023

Preprint

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Networks are essential for analyzing complex systems. However, their growing size necessitates backbone extraction techniques aimed at reducing their size while retaining critical features. In practice, selecting, implementing, and evaluating the most suitable backbone extraction method may be challenging. This paper introduces netbone, a Python package designed for assessing the performance of backbone extraction techniques in weighted networks. Its comparison framework is the standout feature of netbone. Indeed, the tool incorporates state-of-the-art backbone extraction techniques. Furthermore, it provides a comprehensive suite of evaluation metrics allowing users to evaluate different backbones techniques and select the best one based on their case study. We illustrate the flexibility and effectiveness of netbone through the US air transportation network analysis. We compare the performance of different backbone extraction techniques using the evaluation metrics. We also show how users can integrate a new backbone extraction method into the comparison framework. Netbone is publicly available as an open-source tool, ensuring its accessibility to researchers and practitioners. Promoting standardized evaluation practices contributes to the advancement of backbone extraction techniques and fosters reproducibility and comparability in research efforts. We anticipate that netbone will serve as a valuable resource for researchers and practitioners enabling them to make informed decisions when selecting backbone extraction techniques to gain insights into the structural and functional properties of complex systems.

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An evaluation tool for backbone extraction techniques in weighted complex networks

Yassin,

Haidar,

Cherifi

et al. 2023

Sci Rep

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Networks are essential for analyzing complex systems. However, their growing size necessitates backbone extraction techniques aimed at reducing their size while retaining critical features. In practice, selecting, implementing, and evaluating the most suitable backbone extraction method may be challenging. This paper introduces , a Python package designed for assessing the performance of backbone extraction techniques in weighted networks. Its comparison framework is the standout feature of . Indeed, the tool incorporates state-of-the-art backbone extraction techniques. Furthermore, it provides a comprehensive suite of evaluation metrics allowing users to evaluate different backbones techniques. We illustrate the flexibility and effectiveness of through the US air transportation network analysis. We compare the performance of different backbone extraction techniques using the evaluation metrics. We also show how users can integrate a new backbone extraction method into the comparison framework. is publicly available as an open-source tool, ensuring its accessibility to researchers and practitioners. Promoting standardized evaluation practices contributes to the advancement of backbone extraction techniques and fosters reproducibility and comparability in research efforts. We anticipate that will serve as a valuable resource for researchers and practitioners enabling them to make informed decisions when selecting backbone extraction techniques to gain insights into the structural and functional properties of complex systems.

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Dynamic Network of United States Air Transportation at Multiple Levels

Cited by 3 publications

References 21 publications

On network backbone extraction for modeling online collective behavior

On network backbone extraction for modeling online collective behavior

An Evaluation Tool for Backbone Extraction Techniques in Weighted Complex Networks

An evaluation tool for backbone extraction techniques in weighted complex networks

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