Unbiased degree-preserving randomization of directed binary networks

Roberts, E. S.; Coolen, Anthony C. C.

doi:10.1103/physreve.85.046103

Cited by 48 publications

(73 citation statements)

References 30 publications

(84 reference statements)

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“…the Reciprocated Configuration Model (RCM) [28,32]. A first way of implementing a null model is computational [30,33]. For instance, in the CM and DCM, one starts with the original network and randomizes it through the iteration of some fundamental rewiring move that alters the topology but keeps the (in-and out-) degrees of all vertices fixed [30].…”

Section: A Measuring Global Spatial Effectsmentioning

confidence: 99%

“…For instance, in the CM and DCM, one starts with the original network and randomizes it through the iteration of some fundamental rewiring move that alters the topology but keeps the (in-and out-) degrees of all vertices fixed [30]. As found recently [33], this approach produces biased results unless one uses a careful (but difficult to implement) acceptance probability of the attempted rewiring moves. Even with the correct acceptance probability, this approach is extremely time consuming since many iterations are necessary in order to produce a single randomized network, and many such randomized network variants are needed in order to sample the microcanonical ensemble of random graphs with degree sequence exactly equal to the observed one.…”

Section: A Measuring Global Spatial Effectsmentioning

confidence: 99%

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Spatial effects in real networks: Measures, null models, and applications

et al. 2012

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Spatially embedded networks are shaped by a combination of purely topological (spaceindependent) and space-dependent formation rules. While it is quite easy to artificially generate networks where the relative importance of these two factors can be varied arbitrarily, it is much more difficult to disentangle these two architectural effects in real networks. Here we propose a solution to the problem by introducing global and local measures of spatial effects that, through a comparison with adequate null models, effectively filter out the spurious contribution of non-spatial constraints. Our filtering allows us to consistently compare different embedded networks or different historical snapshots of the same network. As a challenging application we analyse the World Trade Web, whose topology is expected to depend on geographic distances but is also strongly determined by non-spatial constraints (degree sequence or GDP). Remarkably, we are able to detect weak but significant spatial effects both locally and globally in the network, showing that our method succeeds in retrieving spatial information even when non-spatial factors dominate. We finally relate our results to the economic literature on gravity models and trade globalization.

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Section: A Measuring Global Spatial Effectsmentioning

confidence: 99%

Section: A Measuring Global Spatial Effectsmentioning

confidence: 99%

Spatial effects in real networks: Measures, null models, and applications

et al. 2012

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“…The Maslov-Sneppen method might be biased in dense networks because there are no degrees of freedom for reshuffling (Roberts and Coolen, 2012). Given that we selected a balanced cross-section, the pool of trading world countries was notably reduced.…”

Section: Predicting the Architecture: Null Versus Gravity Modelmentioning

confidence: 99%

Global Trade Imbalances: A Network Approach

Dueñas

Fagiolo

2014

Advs. Complex Syst.

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We study trade imbalances between world countries in the period 1960-2000 using a complex-network approach. We show that trade imbalances in absolute value are characterized by a hierarchical arrangement wherein few rich economies display high clustering and carry an important amount of global-trade imbalances. In contrast, trade imbalances in relative terms show a more fragmented topology, with less concentrated clustering which is particularly high for emergent economies. In addition, we find that traditional null random-network models and the gravity model poorly predict the topology of trade imbalance networks. Our main finding is that the evolution of the international trade has caused very heterogeneous imbalances in world economies, which may have important consequences for global instability and development.

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“…As a null model we now consider a random network characterized by a strength distribution equal to the one observed empirically for each time period (see (46) and references therein). For directed and weighted representations we can construct a randomization using the edge swap procedure (that now conserves the vertex in-out strength sequence but not the in-out degree sequence) in the following way.…”

Section: 21mentioning

confidence: 99%

Quantifying preferential trading in the e-MID interbank market

Hatzopoulos

Iori

Mantegna

et al. 2014

Quantitative Finance

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Interbank markets allow credit institutions to exchange capital for purposes of liquidity management. These markets are among the most liquid markets in the financial system. However, liquidity of interbank markets dropped during the 2007-2008 financial crisis, and such a lack of liquidity influenced the entire economic system. In this paper, we analyze transaction data from the e-MID market which is the only electronic interbank market in the Euro Area and US, over a period of eleven years (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)). We adapt a method developed to detect statistically validated links in a network, in order to reveal preferential trading in a directed network. Preferential trading between banks is detected by comparing empirically observed trading relationships with a null hypothesis that assumes random trading among banks doing a heterogeneous number of transactions. Preferential trading patterns are revealed at time windows of 3-maintenance periods. We show that preferential trading is observed throughout the whole period of analysis and that the number of preferential trading links does not show any significant trend in time, in spite of a decreasing trend in the number of pairs of banks making transactions. We observe that preferential trading connections typically involve large trading volumes. During the crisis, we also observe that transactions occurring between banks with a preferential connection occur at larger interest rates than the complement set -an effect that is not observed before the crisis. Permanent repository link

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Unbiased degree-preserving randomization of directed binary networks

Cited by 48 publications

References 30 publications

Spatial effects in real networks: Measures, null models, and applications

Spatial effects in real networks: Measures, null models, and applications

Global Trade Imbalances: A Network Approach

Quantifying preferential trading in the e-MID interbank market

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