Comparing complex networks: in defence of the simple

Martínez, Johann H.; Chávez, Mario

doi:10.1088/1367-2630/ab0065

Cited by 10 publications

(7 citation statements)

References 24 publications

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“…the probability of following a particular trajectory from node i to j is almost equal to the probability of going back on the same trajectory from j to i. This can be seen because the random walk fulfills approximately the detailed balance condition (7), since }ΠM ´ΠM T } F {}ΠM } F " 0.03, where Π " diag pπq and } ¨}F denotes the Frobenius norm [29]. This near time-reversibility coincides with the intuition that most journeys in the mobility network are based on commuting travel patterns.…”

Section: Characteristics Of the Baseline Mobility Networkmentioning

confidence: 95%

Multiscale mobility patterns and the restriction of human movement

Schindler¹,

Clarke²,

Barahona³

2022

Preprint

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Strict lockdown measures have been put in place in many countries around the world to constrain human mobility in response to the unparalleled challenges posed by the COVID-19 pandemic. Here we apply networktheoretic tools to analyse a geolocalised dataset of human mobility of 16 million UK Facebook users from March to July 2020. A special emphasis lies on dynamical perspectives of network analysis and multi-scale community detection with Markov Stability analysis is performed to identify signatures for the mobility contraction in the UK. Thereby, a new quantitative criterion for the scale selection in Markov Stability analysis is proposed, which reveals different scales of mobility in a semi-automated manner. The analysis of the UK mobility network reveals a pronounced decline of human mobility under COVID-19 and suggests that local community structure has been strengthened under lockdown. In particular, human mobility does not follow along purely geographic and administrative lines but the flow-based approach allows for the identification of intrinsic mobility patterns that may inform future interventions to prevent COVID-19 transmission.

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Section: Characteristics Of the Baseline Mobility Networkmentioning

confidence: 95%

Multiscale mobility patterns and the restriction of human movement

Schindler¹,

Clarke²,

Barahona³

2022

Preprint

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“…Connectedness is described by the network degree (also called centrality degree), which defines the number of edges incident on a certain node, that is how many catchments a certain catchment is correlated with. Connectedness length is defined for each existing edge as the Euclidean distance (Martinez & Chavez, 2019) between catchment outlets of flow‐dependent but not necessarily physically connected catchments. We compare overall network degree and connectedness length for different network types by comparing their degree and connectedness length distributions and the medians of these distributions.…”

Section: Methodsmentioning

confidence: 99%

Complex High‐ and Low‐Flow Networks Differ in Their Spatial Correlation Characteristics, Drivers, and Changes

Brunner

Gilleland

2021

Water Resources Research

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Hydrologic extremes such as floods and droughts are often spatially related, which increases management challenges and potential impacts. However, these spatial relationships in high and low flows are often overlooked in risk assessments and we know little about their differences and origins. Here, we ask how spatial relationships of both types of hydrologic extremes and their potential hydro-meteorological drivers differ and vary by season. We propose lagged upper-and lower-tail correlation as a measure of extremal dependence for temporally ordered events to build complex networks of high and low flows.We compare complex networks of overall, low and high flows, determine hydro-meteorological drivers of these networks, and map past changes in spatial relationships using a largesample data set in Central Europe. Our network comparison shows that low flows are correlated more strongly and over longer distances than high flows and high-and lowflow networks are strongest in spring and weakest in summer. Our driver analysis shows that high-flow dependence is most strongly governed by precipitation in winter and evapotranspiration in summer while low-flow dependence is most strongly governed by snowmelt in winter and evapotranspiration in fall. Finally, our change analysis shows that changes in connectedness (i.e. the number of catchments a catchments shows strong flow correlations with) vary spatially and are mostly positive for high flows. We conclude that spatial flow correlations are considerable for both high and particularly low flows as a result of a combination of spatially related hydro-meteorological drivers whose importance varies by extreme type and season.

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“…how many catchments a certain catchment is connected with. Connectedness length is defined for each existing edge as the Euclidean distance between catchment outlets of flood-dependent but not necessarily physically connected catchments (Martinez and Chavez 2019). We compare the overall network degree and connectedness length for the different flood types by comparing the distributions of degree and connectedness length across catchments for the different flood types.…”

Section: Spatial Dependencementioning

confidence: 99%

Snow-influenced floods are more strongly connected in space than purely rainfall-driven floods

Brunner

Fischer

2022

Environ. Res. Lett.

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Widespread floods that affect several catchments are associated with large damages and costs. To improve flood protection, a better understanding of the driving processes of such events is needed. Here, we assess how spatial flood connectedness varies with the flood generation process using a flood event classification scheme that distinguishes between rainfall-driven and snowmelt-influenced flood types. Our results show that the dominant flood generation processes in Europe vary by region, season, and event severity. Specifically, we show that severe floods are more often associated with snow-related processes than moderate events. In addition, we find that snow-influenced events show stronger spatial connections than rainfall-driven events. The spatial connectedness of rainfall-driven events depends on the rainfall duration, and the connectedness decreases with increasing duration. These findings have potential implications for flood risk in a warming climate, both locally and regionally. The projected decrease in the frequency of occurrence of snowmelt-influenced floods may translate into a decrease in the frequency of severe and widespread floods in catchments where snowmelt processes are important for flood generation.

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Comparing complex networks: in defence of the simple

Cited by 10 publications

References 24 publications

Multiscale mobility patterns and the restriction of human movement

Multiscale mobility patterns and the restriction of human movement

Complex High‐ and Low‐Flow Networks Differ in Their Spatial Correlation Characteristics, Drivers, and Changes

Snow-influenced floods are more strongly connected in space than purely rainfall-driven floods

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