IMT Institutional Repository: No conditions. Results ordered -Date Deposited. 2024-03-28T23:34:10ZEPrintshttp://eprints.imtlucca.it/images/logowhite.pnghttp://eprints.imtlucca.it/2018-01-15T08:04:11Z2018-01-15T08:04:11Zhttp://eprints.imtlucca.it/id/eprint/3857This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/38572018-01-15T08:04:11ZSpatio-Temporal Patterns of the International Merger and Acquisition NetworkThis paper analyses the world web of mergers and acquisitions (M&As) using a complex network approach. We use data of
M&As to build a temporal sequence of binary and weighted-directed networks for the period 1995-2010 and 224 countries
(nodes) connected according to their M&As flows (links). We study different geographical and temporal aspects of the international
M&A network (IMAN), building sequences of filtered sub-networks whose links belong to specific intervals of distance
or time. Given that M&As and trade are complementary ways of reaching foreign markets, we perform our analysis using
statistics employed for the study of the international trade network (ITN), highlighting the similarities and differences between
the ITN and the IMAN. In contrast to the ITN, the IMAN is a low density network characterized by a persistent giant component
with many external nodes and low reciprocity. Clustering patterns are very heterogeneous and dynamic. High-income
economies are the main acquirers and are characterized by high connectivity, implying that most countries are targets of a few
acquirers. Like in the ITN, geographical distance strongly impacts the structure of the IMAN: link-weights and node degrees
have a non-linear relation with distance, and an assortative pattern is present at short distances.Marco DuenasRossana Mastrandrearossana.mastrandrea@imtlucca.itMatteo BarigozziGiorgio Fagiolo2017-08-04T08:43:58Z2017-08-04T08:43:58Zhttp://eprints.imtlucca.it/id/eprint/3738This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/37382017-08-04T08:43:58ZRiver Networks and Optimal Channel NetworksRiver networks represent a perfect example of a physical
phenomenon that can be described by means of graph theory.
Water collected by rainfall flows from one point to another
one (downstream) in the river basin creates a spanning (water
flows uniformly on the terrain and therefore from every point
of the basin we have water flow) tree (water cannot flow uphill).
Rivers on Earth and even those that might have been
present on Mars all display similar statistical properties
thereby calling for a model based on basic properties.
A class of models named Optimal Channel Networks (OCN) derive
the final configuration by minimising a given cost function.
The physical inspiration for the minimization problem traces
back to the ideas of Nobel laureate Prigogine on a general
theory of irreversible processes in open dissipative systems.
Actually, theoretical results from OCN allowed to provide an
explanation to universal allometric behaviour in a variety
of different physical situations from species distribution to food webs optimisation alternative to the traditional
approach. In the specific case of river networks, the OCN
model postulates that the total gravitational energy loss in the
system is minimised. Empirical and theoretical works focus
generally on two dimensional case, while recently (inspired by
vascular systems) also the three dimensional case has been
analysed.
Here we devise some new analytical results that illustrate
the role and the properties of the structure that minimises
the cost function proposed in the ABM and we also provide
some insight about the structure of the absolute minimum by varying some of the parameters of the model. In what follows we will give a theoretical characterization of river networks and provide a simple rule to distinguish spanning trees from natural river trees. Furthermore, we extend the study of OCNs embedded on a lattice finding a lower and upper bound for the energy of an OCN in any dimension D.Paul BalisterJószef BaloghBéla BollobásGuido Caldarelliguido.caldarelli@imtlucca.itRossana Mastrandrearossana.mastrandrea@imtlucca.itRob Morris2017-08-04T07:29:15Z2017-08-04T07:29:15Zhttp://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 Ruzzenenti2017-08-03T07:18:17Z2017-08-03T07:18:17Zhttp://eprints.imtlucca.it/id/eprint/3732This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/37322017-08-03T07:18:17ZOrganization and hierarchy of the human functional brain network lead to a chain-like coreThe brain is a paradigmatic example of a complex system: its functionality emerges as a global property of local mesoscopic and microscopic interactions. Complex network theory allows to elicit the functional architecture of the brain in terms of links (correlations) between nodes (grey matter regions) and to extract information out of the noise. Here we present the analysis of functional magnetic resonance imaging data from forty healthy humans at rest for the investigation of the basal scaffold of the functional brain network organization. We show how brain regions tend to coordinate by forming ahighly hierarchical chain-like structure of homogeneously clustered anatomical areas. A maximum spanning tree approach revealed the centrality of the occipital cortex and the peculiar aggregation of
cerebellar regions to form a closed core. We also report the hierarchy of network segregation and the level of clusters integration as a function of the connectivity strength between brain regions.Rossana Mastrandrearossana.mastrandrea@imtlucca.itAndrea GabrielliFabrizio PirasGianfranco SpallettaGuido Caldarelliguido.caldarelli@imtlucca.itTommaso Gili2016-06-28T13:34:12Z2016-06-28T13:34:12Zhttp://eprints.imtlucca.it/id/eprint/3508This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/35082016-06-28T13:34:12ZHow to estimate epidemic risk from incomplete contact
diaries data?Social interactions shape the patterns of spreading processes in a population. Techniques such as diaries or proximity sensors allow to collect data about encounters and to build networks of contacts
between individuals. The contact networks obtained from these different techniques are however quantitatively different. Here, we first show how these discrepancies affect the prediction of the
epidemic risk when these data are fed to numerical models of epidemic spread: low participation rate, under-reporting of contacts and overestimation of contact durations in contact diaries with
respect to sensor data determine indeed important differences in the outcomes of the corresponding simulations with for instance an enhanced sensitivity to initial conditions. Most importantly, we
investigate if and how information gathered from contact diaries can be used in such simulations in order to yield an accurate description of the epidemic risk, assuming that data from sensors represent the ground truth. The contact networks built from contact sensors and diaries present indeed several structural similarities: this suggests the possibility to construct, using only the contact diary network information, a surrogate contact network such that simulations using this surrogate network give the same estimation of the epidemic risk as simulations using the contact sensor network. We present and compare several methods to build such surrogate data, and show
that it is indeed possible to obtain a good agreement between the outcomes of simulations using surrogate and sensor data, as long as the contact diary information is complemented by publicly
available data describing the heterogeneity of the durations of human contacts.Rossana Mastrandrearossana.mastrandrea@imtlucca.itAlain Barrat2015-11-09T15:14:26Z2015-11-09T15:14:26Zhttp://eprints.imtlucca.it/id/eprint/2858This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/28582015-11-09T15:14:26ZEnhancing the evaluation of pathogen transmission risk in a hospital by merging hand-hygiene compliance and contact data: a proof-of-concept studyBACKGROUND:Hand-hygiene compliance and contacts of health-care workers largely determine the potential paths of pathogen transmission in hospital wards. We explored how the combination of data collected by two automated infrastructures based on wearable sensors and recording (1) use of hydro-alcoholic solution and (2) contacts of health-care workers provide an enhanced view of the risk of transmission events in the ward. METHODS:We perform a proof-of-concept observational study. Detailed data on contact patterns and hand-hygiene compliance of health-care workers were collected by wearable sensors over 12days in an infectious disease unit of a hospital in Marseilles, France.RESULTS:10,837 contact events among 10 doctors, 4 nurses, 4 nurses' aids and 4 housekeeping staff were recorded during the study. Most contacts took place among medical doctors. Aggregate contact durations were highly heterogeneous and the resulting contact network was highly structured. 510 visits of health-care workers to patients' rooms were recorded, with a low rate of hand-hygiene compliance. Both data sets were used to construct histories and statistics of contacts informed by the use of hydro-alcoholic solution, or lack thereof, of the involved health-care workers. CONCLUSIONS:Hand-hygiene compliance data strongly enrich the information concerning contacts among health-care workers, by assigning a 'safe' or 'at-risk' value to each contact. The global contact network can thus be divided into 'at-risk' and 'safe' contact networks. The combined data could be of high relevance for outbreak investigation and to inform data-driven models of nosocomial disease spread.Rossana Mastrandrearossana.mastrandrea@imtlucca.itAlberto Soto-AladroPhilippe BrouquiAlain Barrat2015-11-09T15:05:53Z2015-11-09T15:05:53Zhttp://eprints.imtlucca.it/id/eprint/2857This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/28572015-11-09T15:05:53ZContact Patterns in a High School: A Comparison between Data Collected Using Wearable Sensors, Contact Diaries and Friendship SurveysGiven their importance in shaping social networks and determining how information or transmissible diseases propagate in a population, interactions between individuals are the subject of many data collection efforts. To this aim, different methods are commonly used, ranging from diaries and surveys to decentralised infrastructures based on wearable sensors. These methods have each advantages and limitations but are rarely compared in a given setting. Moreover, as surveys targeting friendship relations might suffer less from memory biases than contact diaries, it is interesting to explore how actual contact patterns occurring in day-to-day life compare with friendship relations and with online social links. Here we make progresses in these directions by leveraging data collected in a French high school and concerning (i) face-to-face contacts measured by two concurrent methods, namely wearable sensors and contact diaries, (ii) self-reported friendship surveys, and (iii) online social links. We compare the resulting data sets and find that most short contacts are not reported in diaries while long contacts have a large reporting probability, and that the durations of contacts tend to be overestimated in the diaries. Moreover, measured contacts corresponding to reported friendship can have durations of any length but all long contacts do correspond to a reported friendship. On the contrary, online links that are not also reported in the friendship survey correspond to short face-to-face contacts, highlighting the difference of nature between reported friendships and online links. Diaries and surveys suffer moreover from a low sampling rate, as many students did not fill them, showing that the sensor-based platform had a higher acceptability. We also show that, despite the biases of diaries and surveys, the overall structure of the contact network, as quantified by the mixing patterns between classes, is correctly captured by both networks of self-reported contacts and of friendships, and we investigate the correlations between the number of neighbors of individuals in the three networks. Overall, diaries and surveys tend to yield a correct picture of the global structural organization of the contact network, albeit with much less links, and give access to a sort of backbone of the contact network corresponding to the strongest links, i.e., the contacts of longest cumulative durations.Rossana Mastrandrearossana.mastrandrea@imtlucca.itJulie FournetAlain Barrat2015-11-05T14:03:25Z2018-03-08T16:58:46Zhttp://eprints.imtlucca.it/id/eprint/2827This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/28272015-11-05T14:03:25ZUnbiased sampling of network ensemblesSampling random graphs with given properties is a key step in the analysis of networks, as random ensembles represent basic null models required to identify patterns such as communities and motifs. An important requirement is that the sampling process is unbiased and efficient. The main approaches are microcanonical, i.e. they sample graphs that match the enforced constraints exactly. Unfortunately, when applied to strongly heterogeneous networks (like most real-world examples), the majority of these approaches become biased and/or time-consuming. Moreover, the algorithms defined in the simplest cases, such as binary graphs with given degrees, are not easily generalizable to more complicated ensembles. Here we propose a solution to the problem via the introduction of a ‘Maximize and Sample’ (‘Max & Sam’ for short) method to correctly sample ensembles of networks where the constraints are ‘soft’, i.e. realized as ensemble averages. Our method is based on exact maximum-entropy distributions and is therefore unbiased by construction, even for strongly heterogeneous networks. It is also more computationally efficient than most microcanonical alternatives. Finally, it works for both binary and weighted networks with a variety of constraints, including combined degree-strength sequences and full reciprocity structure, for which no alternative method exists. Our canonical approach can in principle be turned into an unbiased microcanonical one, via a restriction to the relevant subset. Importantly, the analysis of the fluctuations of the constraints suggests that the microcanonical and canonical versions of all the ensembles considered here are not equivalent. We show various real-world applications and provide a code implementing all our algorithms.Tiziano Squartinitiziano.squartini@imtlucca.itRossana Mastrandrearossana.mastrandrea@imtlucca.itDiego Garlaschellidiego.garlaschelli@imtlucca.it2015-11-05T13:48:25Z2018-03-08T16:59:14Zhttp://eprints.imtlucca.it/id/eprint/2825This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/28252015-11-05T13:48:25ZReconstructing the world trade multiplex: The role of intensive and extensive biasesIn economic and financial networks, the strength of each node has always an important economic meaning, such as the size of supply and demand, import and export, or financial exposure. Constructing null models of networks matching the observed strengths of all nodes is crucial in order to either detect interesting deviations of an empirical network from economically meaningful benchmarks or reconstruct the most likely structure of an economic network when the latter is unknown. However, several studies have proved that real economic networks and multiplexes topologically differ from configurations inferred only from node strengths. Here we provide a detailed analysis of the world trade multiplex by comparing it to an enhanced null model that simultaneously reproduces the strength and the degree of each node. We study several temporal snapshots and almost 100 layers (commodity classes) of the multiplex and find that the observed properties are systematically well reproduced by our model. Our formalism allows us to introduce the (static) concept of extensive and intensive bias, defined as a measurable tendency of the network to prefer either the formation of extra links or the reinforcement of link weights, with respect to a reference case where only strengths are enforced. Our findings complement the existing economic literature on (dynamic) intensive and extensive trade margins. More generally, they show that real-world multiplexes can be strongly shaped by layer-specific local constraints.Rossana Mastrandrearossana.mastrandrea@imtlucca.itTiziano Squartinitiziano.squartini@imtlucca.itGiorgio FagioloDiego Garlaschellidiego.garlaschelli@imtlucca.it2015-11-05T13:08:20Z2018-03-08T17:00:11Zhttp://eprints.imtlucca.it/id/eprint/2823This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/28232015-11-05T13:08:20ZEnhanced reconstruction of weighted networks from strengths and degreesNetwork topology plays a key role in many phenomena, from the spreading of diseases to that of financial crises. Whenever the whole structure of a network is unknown, one must resort to reconstruction methods that identify the least biased ensemble of networks consistent with the partial information available. A challenging case, frequently encountered due to privacy issues in the analysis of interbank flows and Big Data, is when there is only local (node-specific) aggregate information available. For binary networks, the relevant ensemble is one where the degree (number of links) of each node is constrained to its observed value. However, for weighted networks the problem is much more complicated. While the naïve approach prescribes to constrain the strengths (total link weights) of all nodes, recent counter-intuitive results suggest that in weighted networks the degrees are often more informative than the strengths. This implies that the reconstruction of weighted networks would be significantly enhanced by the specification of both strengths and degrees, a computationally hard and bias-prone procedure. Here we solve this problem by introducing an analytical and unbiased maximum-entropy method that works in the shortest possible time and does not require the explicit generation of reconstructed samples. We consider several real-world examples and show that, while the strengths alone give poor results, the additional knowledge of the degrees yields accurately reconstructed networks. Information-theoretic criteria rigorously confirm that the degree sequence, as soon as it is non-trivial, is irreducible to the strength sequence. Our results have strong implications for the analysis of motifs and communities and whenever the reconstructed ensemble is required as a null model to detect higher-order patterns.Rossana Mastrandrearossana.mastrandrea@imtlucca.itTiziano Squartinitiziano.squartini@imtlucca.itGiorgio FagioloDiego Garlaschellidiego.garlaschelli@imtlucca.it