IMT Institutional Repository: No conditions. Results ordered -Date Deposited. 2024-05-21T19:09:16ZEPrintshttp://eprints.imtlucca.it/images/logowhite.pnghttp://eprints.imtlucca.it/2017-08-04T07:29:15Z2017-08-04T07:29:15Zhttp://eprints.imtlucca.it/id/eprint/3737This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/37372017-08-04T07:29:15ZWho's who in global value chains? A weighted
network approachThis paper represents global value chains (GVCs) as weighted networks of foreign value
added in exports, which allows for the identification of the specific roles of countries and
for the quantification of their relative importance over time. A major structural change
occurred in the beginning of the century as GVCs steadily turned into global networks,
amid an unprecedented growth of value-added flows and the rise of China as a major
player. First-order network metrics highlight the vital but also distinct roles of Germany,
the US, China and Japan in the international organisation of production. Germany is very relevant both as a user and as a supplier of foreign inputs, while the US acts mostly as a supplier of value added to other countries. Second-order properties of networks shed light on the complex architecture of GVCs, notably in terms of cyclical triangular relationships. Germany's GVCs mostly root in direct relationships, while Japanese ones typically involve more than two countries.João AmadorSónia CabralRossana Mastrandrearossana.mastrandrea@imtlucca.itFranco Ruzzenenti2015-11-05T15:07:16Z2018-03-08T16:58:21Zhttp://eprints.imtlucca.it/id/eprint/2836This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/28362015-11-05T15:07:16ZMultiplexity and multireciprocity in directed multiplexesIn recent years, the study of multi-layer networks has received much attention. Here, we provide new measures of dependency between directed links across different layers of multiplex networks. We show that this operation requires more than a straightforward extension of the corresponding multiplexity measures that have been developed for undirected multiplexes. In particular, one should take into account the effects of reciprocity, i.e. the tendency of pairs of vertices to establish mutual connections. We extend this quantity to multiplexes and introduce the notion of multireciprocity, defined as the tendency of links in one layer to be reciprocated by links in a different layer. While ordinary reciprocity reduces to a scalar quantity, multireciprocity requires a square matrix generated by all the possible pairs of layers. We introduce multireciprocity metrics valid for both binary and weighted networks and then measure these quantities on the World Trade Multiplex (WTM), representing the import-export relationships between world countries in different products. We show that several pairs of layers exhibit strong multiplexity, an effect which can however be largely encoded into the degree or strength sequences of individual layers. We also find that most pairs of commodities are characterised by positive multireciprocity, and that such values are significantly lower than the usual reciprocity measured on the aggregated network. We finally identify robust empirical patterns that allow us to use the multireciprocity matrix to retrieve the two-layer reciprocated degree (strength) of a node from the ordinary in-degree (in-strength) in a single layer and to reconstruct joint multi-layer connection probabilities from marginal ones, hence bridging the gap between single-layer properties and truly multiplex information.Valerio GemmettoTiziano Squartinitiziano.squartini@imtlucca.itFrancesco PiccioloFranco RuzzenentiDiego Garlaschellidiego.garlaschelli@imtlucca.it2015-11-05T12:59:14Z2018-03-08T16:59:55Zhttp://eprints.imtlucca.it/id/eprint/2821This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/28212015-11-05T12:59:14ZDisentangling spatial and non-spatial effects in real networksTiziano Squartinitiziano.squartini@imtlucca.itFrancesco PiccioloFranco RuzzenentiDiego Garlaschellidiego.garlaschelli@imtlucca.itRiccardo Basosi2015-11-05T11:58:38Z2018-03-08T17:02:35Zhttp://eprints.imtlucca.it/id/eprint/2818This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/28182015-11-05T11:58:38ZReciprocity of weighted networksIn directed networks, reciprocal links have dramatic effects on dynamical processes, network growth, and higher-order structures such as motifs and communities. While the reciprocity of binary networks has been extensively studied, that of weighted networks is still poorly understood, implying an ever-increasing gap between the availability of weighted network data and our understanding of their dyadic properties. Here we introduce a general approach to the reciprocity of weighted networks, and define quantities and null models that consistently capture empirical reciprocity patterns at different structural levels. We show that, counter-intuitively, previous reciprocity measures based on the similarity of mutual weights are uninformative. By contrast, our measures allow to consistently classify different weighted networks according to their reciprocity, track the evolution of a network's reciprocity over time, identify patterns at the level of dyads and vertices, and distinguish the effects of flux (im)balances or other (a)symmetries from a true tendency towards (anti-)reciprocation.Tiziano Squartinitiziano.squartini@imtlucca.itFrancesco PiccioloFranco RuzzenentiDiego Garlaschellidiego.garlaschelli@imtlucca.it2015-11-05T11:48:40Z2018-03-08T17:03:03Zhttp://eprints.imtlucca.it/id/eprint/2817This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/28172015-11-05T11:48:40ZThe Role of Distances in the World Trade WebIn the economic literature, geographic distances are considered fundamental factors to be included in any theoretical model whose aim is the quantification of the trade between countries. Quantitatively, distances enter into the so-called gravity models that successfully predict the weight of non-zero trade flows. However, it has been recently shown that gravity models fail to reproduce the binary topology of the World Trade Web. In this paper a different approach is presented: the formalism of exponential random graphs is used and the distances are treated as constraints, to be imposed on a previously chosen ensemble of graphs. Then, the information encoded in the geographical distances is used to explain the binary structure of the World Trade Web, by testing it on the degree-degree correlations and the reciprocity structure. This leads to the definition of a novel null model that combines spatial and non-spatial effects. The effectiveness of spatial constraints is compared to that of nonspatial ones by means of the Akaike Information Criterion and the Bayesian Information Criterion. Even if it is commonly believed that the World Trade Web is strongly dependent on the distances, what emerges from our analysis is that distances do not play a crucial role in shaping the World Trade Web binary structure and that the information encoded into the reciprocity is far more useful in explaining the observed patterns.Francesco PiccioloFranco RuzzenentiRiccardo BasosiTiziano Squartinitiziano.squartini@imtlucca.itDiego Garlaschellidiego.garlaschelli@imtlucca.it