Transport network backbone extraction: A comparison of techniques

Dai, Liang; Derudder, Ben; Liu, Xingjian

doi:10.1016/j.jtrangeo.2018.05.012

Cited by 29 publications

(22 citation statements)

References 56 publications

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“…However, geographers and spatial analysts often study weighted networks that are not bipartite projections, for example, transportation networks where edge weights convey capacity or volume. Therefore, future versions of backbone would benefit from implementing some of the already existing methods for extracting the spatial backbone from such non-projection weighted networks (e.g., Dai, Derudder, & Liu, 2018;Dianati, 2016;Serrano et al, 2009). Second, al-though the backbone package implements several different backbone models, the selection of a particular model is left to the user.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Spatial Networks From Bipartite Projections Using the R Backbone Package

Neal

Domagalski

Sagan

2021

Geographical Analysis

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Bipartite projections have become a common way to measure spatial networks. They are now used in many subfields of geography, and are among the most common ways to measure the world city network, where intercity links are inferred from firm co-location patterns. Bipartite projections are attractive because a network can be indirectly inferred from readily available data. However, spatial bipartite projections are difficult to analyze because the links in these networks are weighted, and larger weights do not necessarily indicate stronger or more important connections. Methods for extracting the backbone of bipartite projections offer a solution by using statistical models for identifying the links that have statistically significant weights. In this paper, we introduce the open-source backbone R package, which implements several backbone models, and demonstrate its key features by using it to measure a world city network.

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Section: Discussionmentioning

confidence: 99%

Analysis of Spatial Networks From Bipartite Projections Using the R Backbone Package

Neal

Domagalski

Sagan

2021

Geographical Analysis

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“…However, geographers and spatial analysts often also study weighted networks that are not bipartite projections, for example, transportation networks where edge weights convey capacity or volume. Although methods exist for extracting the spatial backbone from such non-projection weighted networks (Dai, Derudder, & Liu, 2018;Dianati, 2016;Serrano et al, 2009), they are not yet implemented in the backbone package. However, the package's open-source nature facilitates the addition of such features in future versions.…”

Section: Discussionmentioning

confidence: 99%

Analysis of spatial networks from bipartite projections using the R backbone package

Neal¹,

Domagalski²,

Sagan³

2020

Preprint

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Spatial networks can be difficult to measure. Bipartite projections offer a potential solution by indirectly inferring a network from data that is easier to collect. They are now used in many subfields of geography, and are among the most common ways to measure the world city network, where intercity links are inferred from firm co-location patterns. However, spatial bipartite projections are difficult to analyze because the links in these networks are weighted, and larger weights do not necessarily indicate stronger or more important connections. Methods for extracting the backbone of bipartite projections offer a solution by using statistical models for identifying the links that have statistically significant weights. In this paper, we introduce the open-source backbone R package, which implements several backbone models, and demonstrate its key features by using it to measure a world city network.

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“…In the ETFM, activity values are optimized in a maximum likelihood sense, which requires to recursively solve equation (9). Hence, the ETFM computational cost depends on three factors: the number I of intervals, the number of nodes in the ∆th interval, n(∆), and the number m of recursions.…”

Section: Computational Complexitymentioning

confidence: 99%

“…The computational cost grows linearly with the number of intervals I, since it has to be applied independently to each interval, and with m, which is the number of iterations for the maximum likelihood to converge. On the other hand, it is quadratic in n (∆), because of equation (9). Overall, the ETFM complexity hits O (mIN 2 ) in the worst case scenario, while in the best case scenario, when only one interval is present, it is O (mN 2 ), like in the temporal fitness model [5].…”

Section: Computational Complexitymentioning

confidence: 99%

Reconstructing irreducible links in temporal networks: which tool to choose depends on the network size

2020

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Filtering information in complex networks entails the process of removing interactions explained by a proper null hypothesis and retaining the remaining interactions, which form the backbone network. The reconstructed backbone network depends upon the accuracy and reliability of the available tools, which, in turn, are affected by the specific features of the available dataset. Here, we examine the performance of three approaches for the discovery of backbone networks, in the presence of heterogeneous, time-varying node properties. In addition to the recently proposed evolving activity driven model, we extend two existing approaches (the disparity filter and the temporal fitness model) to tackle time-varying phenomena. Our analysis focuses on the influence of the network size, which was previously shown to be a determining factor for the performance of the evolving activity driven model. Through mathematical and numerical analysis, we propose general guidelines for the use of these three approaches based on the available dataset. For small networks, the evolving temporal fitness model offers a more reasonable trade-off between the number of links assigned to the backbone network and the accuracy of their inference. The main limitation of this methodology lies in its computational cost, which becomes excessively high for large networks. In this case, the evolving activity driven model could be a valid substitute to the evolving temporal fitness model. If one seeks to minimize the number of links inaccurately included in the backbone network at the risk of dismissing many links that could belong to it, then the temporal disparity filter would be the approach-of-choice. Overall, our contribution expands the toolbox of network discovery in the technical literature and should help users in choosing the right network discovery instrument, depending on the problem considered.

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Transport network backbone extraction: A comparison of techniques

Cited by 29 publications

References 56 publications

Analysis of Spatial Networks From Bipartite Projections Using the R Backbone Package

Analysis of Spatial Networks From Bipartite Projections Using the R Backbone Package

Analysis of spatial networks from bipartite projections using the R backbone package

Reconstructing irreducible links in temporal networks: which tool to choose depends on the network size

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