260 results on '"Garlaschelli, Diego"'
Search Results
252. Reconciling econometrics with continuous maximum-entropy network models.
- Author
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Di Vece, Marzio, Garlaschelli, Diego, and Squartini, Tiziano
- Subjects
- *
ECONOMETRICS , *DISTRIBUTION (Probability theory) , *ECONOMIC impact , *WEIGHTED graphs , *ECONOMETRIC models , *MAXIMUM entropy method , *TOPOLOGICAL entropy - Abstract
In the study of economic networks, econometric approaches interpret the traditional Gravity Model specification as the expected link weight coming from a probability distribution whose functional form can be chosen arbitrarily, while statistical-physics approaches construct maximum-entropy distributions of weighted graphs, constrained to satisfy a given set of measurable network properties. In a recent, companion paper, we integrated the two approaches and applied them to the World Trade Web, i.e. the network of international trade among world countries. While the companion paper dealt only with discrete-valued link weights, the present paper extends the theoretical framework to continuous-valued link weights. In particular, we construct two broad classes of maximum-entropy models, namely the integrated and the conditional ones, defined by different criteria to derive and combine the probabilistic rules for placing links and loading them with weights. In the integrated models, both rules follow from a single, constrained optimization of the continuous Kullback–Leibler divergence; in the conditional models, the two rules are disentangled and the functional form of the weight distribution follows from a conditional, optimization procedure. After deriving the general functional form of the two classes, we turn each of them into a proper family of econometric models via a suitable identification of the econometric function relating the corresponding, expected link weights to macroeconomic factors. After testing the two classes of models on World Trade Web data, we discuss their strengths and weaknesses. • We consider the Minimum Discrimination Information Principle. • We derive each network model solving a constrained K-L divergence minimization. • These models are informed by structural constraints and economic factors. • The structural information encoded into the degrees cannot be sacrificed. • Weighted information, instead, can be accounted for by purely economic factors. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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253. Phiclust: a clusterability measure for single-cell transcriptomics reveals phenotypic subpopulations
- Author
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Mircea, Maria, Hochane, Mazène, Fan, Xueying, Chuva de Sousa Lopes, Susana M., Garlaschelli, Diego, and Semrau, Stefan
- Abstract
The ability to discover new cell phenotypes by unsupervised clustering of single-cell transcriptomes has revolutionized biology. Currently, there is no principled way to decide whether a cluster of cells contains meaningful subpopulations that should be further resolved. Here, we present phiclust (ϕclust), a clusterability measure derived from random matrix theory that can be used to identify cell clusters with non-random substructure, testably leading to the discovery of previously overlooked phenotypes.
- Published
- 2022
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254. Local Phase Transitions in a Model of Multiplex Networks with Heterogeneous Degrees and Inter-Layer Coupling.
- Author
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Bayrakdar, Nedim, Gemmetto, Valerio, and Garlaschelli, Diego
- Subjects
- *
PHASE transitions , *ISING model , *CRITICAL point (Thermodynamics) , *MAXIMUM entropy method , *INTERNATIONAL trade , *FEMTOCELLS - Abstract
Multilayer networks represent multiple types of connections between the same set of nodes. Clearly, a multilayer description of a system adds value only if the multiplex does not merely consist of independent layers. In real-world multiplexes, it is expected that the observed inter-layer overlap may result partly from spurious correlations arising from the heterogeneity of nodes, and partly from true inter-layer dependencies. It is therefore important to consider rigorous ways to disentangle these two effects. In this paper, we introduce an unbiased maximum entropy model of multiplexes with controllable intra-layer node degrees and controllable inter-layer overlap. The model can be mapped to a generalized Ising model, where the combination of node heterogeneity and inter-layer coupling leads to the possibility of local phase transitions. In particular, we find that node heterogeneity favors the splitting of critical points characterizing different pairs of nodes, leading to link-specific phase transitions that may, in turn, increase the overlap. By quantifying how the overlap can be increased by increasing either the intra-layer node heterogeneity (spurious correlation) or the strength of the inter-layer coupling (true correlation), the model allows us to disentangle the two effects. As an application, we show that the empirical overlap observed in the International Trade Multiplex genuinely requires a nonzero inter-layer coupling in its modeling, as it is not merely a spurious result of the correlation between node degrees across different layers. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
255. Early-warning signals of topological collapse in interbank networks.
- Author
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Tiziano Squartini, van Lelyveld, Iman, and Garlaschelli, Diego
- Subjects
FINANCIAL crises ,TOPOLOGY ,LOOPING (Education) ,FINANCIAL markets ,DEBT - Abstract
The financial crisis clearly illustrated the importance of characterizing the level of 'systemic' risk associated with an entire credit network, rather than with single institutions. However, the interplay between financial distress and topological changes is still poorly understood. Here we analyze the quarterly interbank exposures among Dutch banks over the period 1998-2008, ending with the crisis. After controlling for the link density, many topological properties display an abrupt change in 2008, providing a clear - but unpredictable - signature of the crisis. By contrast, if the heterogeneity of banks' connectivity is controlled for, the same properties show a gradual transition to the crisis, starting in 2005 and preceded by an even earlier period during which anomalous debt loops could have led to the underestimation of counter-party risk. These early-warning signals are undetectable if the network is reconstructed from partial bank-specific data, as routinely done. We discuss important implications for bank regulatory policies. [ABSTRACT FROM AUTHOR]
- Published
- 2013
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256. Interbank network reconstruction enforcing density and reciprocity.
- Author
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Macchiati, Valentina, Mazzarisi, Piero, and Garlaschelli, Diego
- Subjects
- *
SYSTEMIC risk (Finance) , *RECIPROCITY (Psychology) , *DYADS , *CONFIDENTIAL communications , *DENSITY - Abstract
Networks of financial exposures are the key propagators of risk and distress among banks, but their empirical structure is not publicly available because of confidentiality. This limitation has triggered the development of methods of network reconstruction from partial, aggregate information. Unfortunately, even the best methods available fail in replicating the number of directed cycles, which on the other hand play a crucial role in determining graph spectra and hence the degree of network stability and systemic risk. Here we address this challenge by exploiting the hypothesis that the statistics of higher-order cycles is strongly constrained by that of the shortest ones, i.e. by the amount of dyads with reciprocated links. First, we provide a detailed analysis of link reciprocity on the e-MID dataset of Italian banks, finding that correlations between reciprocal links systematically increase with the temporal resolution, typically changing from negative to positive around a timescale of up to 50 days. Then, we propose a new network reconstruction method capable of enforcing, only from the knowledge of aggregate interbank assets and liabilities, both a desired sparsity and a desired link reciprocity. We confirm that the addition of reciprocity dramatically improves the prediction of several structural and spectral network properties, including the largest real eigenvalue and the eccentricity of the elliptical distribution of the other eigenvalues in the complex plane. These results illustrate the importance of correctly addressing the temporal resolution and the resulting level of reciprocity in the reconstruction of financial networks. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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257. Irreducible network backbones: unbiased graph filtering via maximum entropy
- Author
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Gemmetto, Valerio, Cardillo, Alessio, and Garlaschelli, Diego
- Subjects
Social and Information Networks (cs.SI) ,FOS: Computer and information sciences ,Physics - Physics and Society ,FOS: Physical sciences ,Computer Science - Social and Information Networks ,Physics and Society (physics.soc-ph) ,Popular Physics (physics.pop-ph) ,Physics - Popular Physics - Abstract
Networks provide an informative, yet non-redundant description of complex systems only if links represent truly dyadic relationships that cannot be directly traced back to node-specific properties such as size, importance, or coordinates in some embedding space. In any real-world network, some links may be reducible, and others irreducible, to such local properties. This dichotomy persists despite the steady increase in data availability and resolution, which actually determines an even stronger need for filtering techniques aimed at discerning essential links from non-essential ones. Here we introduce a rigorous method that, for any desired level of statistical significance, outputs the network backbone that is irreducible to the local properties of nodes, i.e. their degrees and strengths. Unlike previous approaches, our method employs an exact maximum-entropy formulation guaranteeing that the filtered network encodes only the links that cannot be inferred from local information. Extensive empirical analysis confirms that this approach uncovers essential backbones that are otherwise hidden amidst many redundant relationships and inaccessible to other methods. For instance, we retrieve the hub-and-spoke skeleton of the US airport network and many specialised patterns of international trade. Being irreducible to local transportation and economic constraints of supply and demand, these backbones single out genuinely higher-order wiring principles., Main + SI. (19+8) pages, (8+9) figures, (2+4) tables. Submitted for publication
258. Reconstructing firm-level interactions in the Dutch input–output network from production constraints.
- Author
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Ialongo, Leonardo Niccolò, de Valk, Camille, Marchese, Emiliano, Jansen, Fabian, Zmarrou, Hicham, Squartini, Tiziano, and Garlaschelli, Diego
- Subjects
- *
PROBABILISTIC generative models , *SUPPLY & demand , *BIG data - Abstract
Recent crises have shown that the knowledge of the structure of input–output networks, at the firm level, is crucial when studying economic resilience from the microscopic point of view of firms that try to rewire their connections under supply and demand constraints. Unfortunately, empirical inter-firm network data are protected by confidentiality, hence rarely accessible. The available methods for network reconstruction from partial information treat all pairs of nodes as potentially interacting, thereby overestimating the rewiring capabilities of the system and the implied resilience. Here, we use two big data sets of transactions in the Netherlands to represent a large portion of the Dutch inter-firm network and document its properties. We, then, introduce a generalized maximum-entropy reconstruction method that preserves the production function of each firm in the data, i.e. the input and output flows of each node for each product type. We confirm that the new method becomes increasingly more reliable in reconstructing the empirical network as a finer product resolution is considered and can, therefore, be used as a realistic generative model of inter-firm networks with fine production constraints. Moreover, the likelihood of the model directly enumerates the number of alternative network configurations that leave each firm in its current production state, thereby estimating the reduction in the rewiring capability of the system implied by the observed input–output constraints. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
259. The statistical physics of real-world networks
- Author
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Andrea Gabrielli, Giulio Cimini, Fabio Saracco, Diego Garlaschelli, Guido Caldarelli, Tiziano Squartini, Cimini, Giulio, Squartini, Tiziano, Saracco, Fabio, Garlaschelli, Diego, Gabrielli, Andrea, and Caldarelli, Guido
- Subjects
FOS: Computer and information sciences ,Physics - Physics and Society ,Computer science ,Computer Science - Information Theory ,General Physics and Astronomy ,FOS: Physical sciences ,Physics and Society (physics.soc-ph) ,Information theory ,01 natural sciences ,010305 fluids & plasmas ,Complete information ,0103 physical sciences ,Statistical physics ,010306 general physics ,Condensed Matter - Statistical Mechanics ,Social and Information Networks (cs.SI) ,Settore FIS/03 ,Settore FIS/02 ,Statistical Mechanics (cond-mat.stat-mech) ,Principle of maximum entropy ,Information Theory (cs.IT) ,Local area network ,Computer Science - Social and Information Networks ,Disordered Systems and Neural Networks (cond-mat.dis-nn) ,Condensed Matter - Disordered Systems and Neural Networks ,Complex network ,Scale invariance ,Settore FIS/02 - Fisica Teorica, Modelli e Metodi Matematici ,Null (SQL) ,Heterogeneous network - Abstract
In the last 15 years, statistical physics has been a very successful framework to model complex networks. On the theoretical side, this approach has brought novel insights into a variety of physical phenomena, such as self-organisation, scale invariance, emergence of mixed distributions and ensemble non-equivalence, that display unconventional features on heterogeneous networks. At the same time, thanks to their deep connection with information theory, statistical physics and the principle of maximum entropy have led to the definition of null models for networks reproducing some features of real-world systems, but otherwise as random as possible. We review here the statistical physics approach and the various null models for complex networks, focusing in particular on the analytic frameworks reproducing the local network features. We then show how these models have been used to detect statistically significant and predictive structural patterns in real-world networks, as well as to reconstruct the network structure in case of incomplete information. We further survey the statistical physics models that reproduce more complex, semi-local network features using Markov chain Monte Carlo sampling, as well as the models of generalised network structures such as multiplex networks, interacting networks and simplicial complexes., Comment: accepted version (after revision)
- Published
- 2019
260. Reconstructing firm-level interactions in the Dutch input–output network from production constraints
- Author
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Leonardo Niccolò Ialongo, Camille de Valk, Emiliano Marchese, Fabian Jansen, Hicham Zmarrou, Tiziano Squartini, Diego Garlaschelli, Ialongo, Leonardo Niccolò, de Valk, Camille, Marchese, Emiliano, Jansen, Fabian, Zmarrou, Hicham, Squartini, Tiziano, and Garlaschelli, Diego
- Subjects
production network ,Netherland ,Knowledge ,Multidisciplinary ,Entropy ,network ,input-output economic ,statistical mechanic ,maximum entropy mode ,Settore SECS-S/01 - Statistica ,Settore FIS/02 - Fisica Teorica, Modelli e Metodi Matematici ,Netherlands - Abstract
Recent crises have shown that the knowledge of the structure of input–output networks, at the firm level, is crucial when studying economic resilience from the microscopic point of view of firms that try to rewire their connections under supply and demand constraints. Unfortunately, empirical inter-firm network data are protected by confidentiality, hence rarely accessible. The available methods for network reconstruction from partial information treat all pairs of nodes as potentially interacting, thereby overestimating the rewiring capabilities of the system and the implied resilience. Here, we use two big data sets of transactions in the Netherlands to represent a large portion of the Dutch inter-firm network and document its properties. We, then, introduce a generalized maximum-entropy reconstruction method that preserves the production function of each firm in the data, i.e. the input and output flows of each node for each product type. We confirm that the new method becomes increasingly more reliable in reconstructing the empirical network as a finer product resolution is considered and can, therefore, be used as a realistic generative model of inter-firm networks with fine production constraints. Moreover, the likelihood of the model directly enumerates the number of alternative network configurations that leave each firm in its current production state, thereby estimating the reduction in the rewiring capability of the system implied by the observed input–output constraints.
- Full Text
- View/download PDF
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