Randomized reference models for temporal networks

Gauvin, Laetitia; Génois, Mathieu; Karsai, Márton; Kivelä, Mikko; Takaguchi, Taro; Valdano, Eugenio; Vestergaard, Christian

doi:10.48550/arxiv.1806.04032

Cited by 27 publications

(40 citation statements)

References 87 publications

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“…The surrogate model approach is a statistical proof-by-contradiction method used for investigating specific features and correlations in empirical data sets. It is based on testing composite null hypotheses on data sets that are derived from the empirical data using Monte Carlo methods [44,45,49,50]. A variety of time series [48,[58][59][60] and network data sets [48,[61][62][63] have been analyzed using surrogate models.…”

Section: Surrogate Model Methodsmentioning

confidence: 99%

“…A large ensemble of 5000 realizations is computed for each surrogate model, to reduce the influence of statistical fluctuations. We use the canonical naming convention put forward in [44] to describe the surrogate models M associated with the null hypotheses H 0 . Surrogate models are thus defined by the quantities they conserve with respect to the original empirical data.…”

Section: Surrogate Model Productionmentioning

confidence: 99%

“…The surrogate model method relies on Monte-Carlo-based, data-derived hypothesis tests to analyze specific data features without prescribing concrete underlying mechanisms. Surrogate models have successfully been used as a tool in exploratory data analyses [42,43], particularly for investigating networked processes [44,45], including epidemic and social contagion [46][47][48], and time series data [49,50]. Partially randomizing the empirical data in line with specific null hypotheses, and then comparing key measurements (here, DRF functional shapes) allows us to investigate correlations found in the data, and their causal relevance.…”

Section: Introductionmentioning

confidence: 99%

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Detecting contagious spreading of urban innovations on the global city network

Kitzmann,

Romanczuk,

Donges

2021

Preprint

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Only a fast and global transformation towards decarbonization and sustainability can keep the Earth in a civilization-friendly state. As hotspots for (green) innovation and experimentation, cities could play an important role in this transition. They are also known to profit from each other's ideas, with policy and technology innovations spreading to other cities. In this way, cities can be conceptualized as nodes in a globe-spanning learning network. The dynamics of this process are important for society's response to climate change and other challenges, but remain poorly understood on a macroscopic level. In this contribution, we develop an approach to identify whether network-based complex contagion effects are a feature of sustainability policy adoption by cities, based on dose-response contagion and surrogate data models. We apply this methodology to an example data set, comprising empirical data on the spreading of a public transport innovation (Bus Rapid Transit Systems) and a global inter-city connection network based on scheduled flight routes. We find evidence pointing towards a contagious spreading process which cannot be explained by either the network structure or the increase in global adoption rate alone. This suggests that the actions of a city's abstract "global neighborhood" within the network of cities may be an important factor in which policies and innovations are implemented, with potential connections to the emergence of social tipping processes. The methodology is generic, and can be used to compare the predictive power for innovation spreading of different kinds of inter-city network connections, e.g. via transport links, trade, or co-membership in political networks.

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Section: Surrogate Model Methodsmentioning

confidence: 99%

Section: Surrogate Model Productionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detecting contagious spreading of urban innovations on the global city network

Kitzmann,

Romanczuk,

Donges

2021

Preprint

View full text Add to dashboard Cite

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“…That is, different results of epidemic spreading associated with non-Poissonian IET statistics may owe in large part to whether the structure of the metapopulation network plays a significant role (which would yield heavier-tailed IET distributions) or the population is sufficiently wellmixed (which would yield an exponential IET distribution). The effects of IET statistics other than their heavy-tailed distributions [2,3,82] on epidemic and other dynamical processes may also be due to the underlying metapopulation network. Reexamining the role of IETs in contagion and other dynamical processes from the viewpoint of metapopulation networks warrants future work.…”

Section: Fig 3 CV Of Iet For Various Metapopulation Networkmentioning

confidence: 99%

Metapopulation models imply non-Poissonian statistics of interevent times

Reis

Masuda

2022

Phys. Rev. Research

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Interevent times in temporal contact data from humans and animals typically obey heavy-tailed distributions, and this property impacts contagion and other dynamical processes on networks. We theoretically show that distributions of interevent times heavier-tailed than exponential distributions are a consequence of the most basic metapopulation model used in epidemiology and ecology, in which individuals move from a patch to another according to the simple random walk. Our results hold true irrespectively of the network structure and also for more realistic mobility rules such as high-order random walks and the recurrent mobility patterns used for modeling human dynamics.

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“…Hence, panel (c) of both figures shows the z-scores trends assessing the reliability of ∆-Conformity trends. Z-scores are obtained by comparing point by point the ∆-Conformity score of the original graph to a null distribution of 200 rewired networks using a configuration model [6,18]; We use the following formula for the comparison:…”

Section: /20mentioning

confidence: 99%

Δ-Conformity: Multi-scale Node Assortativity in Feature-rich Stream Graphs

Citraro¹,

Milli²,

Cazabet³

et al. 2021

Preprint

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Heterogeneity is a key aspect of complex networks, often emerging by looking at the distribution of node properties, from the milestone observations on the degree to the recent developments in mixing pattern estimation. Mixing patterns, in particular, refer to nodes' connectivity preferences with respect to an attribute label. Social networks are mostly characterized by assortative/homophilic behaviour, where nodes are more likely to be connected with similar ones. Recently, assortative mixing is increasingly measured in a multi-scale fashion to overcome well-known limitations of classic scores. Such multi-scale strategies can capture heterogeneous behaviors among node homophily, but they ignore an important, often available, addendum in real-world systems: the time when edges are present and the time-varying paths they form accordingly. Hence, temporal homophily is still little understood in complex networks. In this work we aim to cover this gap by introducing the ∆-Conformity measure, a multi-scale, path-aware, node homophily estimator within the new framework of feature-rich stream graphs. A rich experimental section analyzes ∆-Conformity trends over time, spanning the analysis from real-life social interaction networks to a specific case-study about the Bitcoin Transaction Network.

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Randomized reference models for temporal networks

Cited by 27 publications

References 87 publications

Detecting contagious spreading of urban innovations on the global city network

Detecting contagious spreading of urban innovations on the global city network

Metapopulation models imply non-Poissonian statistics of interevent times

Δ-Conformity: Multi-scale Node Assortativity in Feature-rich Stream Graphs

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