2,386 research outputs found
A GDP-driven model for the binary and weighted structure of the International Trade Network
Recent events such as the global financial crisis have renewed the interest
in the topic of economic networks. One of the main channels of shock
propagation among countries is the International Trade Network (ITN). Two
important models for the ITN structure, the classical gravity model of trade
(more popular among economists) and the fitness model (more popular among
networks scientists), are both limited to the characterization of only one
representation of the ITN. The gravity model satisfactorily predicts the volume
of trade between connected countries, but cannot reproduce the observed missing
links (i.e. the topology). On the other hand, the fitness model can
successfully replicate the topology of the ITN, but cannot predict the volumes.
This paper tries to make an important step forward in the unification of those
two frameworks, by proposing a new GDP-driven model which can simultaneously
reproduce the binary and the weighted properties of the ITN. Specifically, we
adopt a maximum-entropy approach where both the degree and the strength of each
node is preserved. We then identify strong nonlinear relationships between the
GDP and the parameters of the model. This ultimately results in a weighted
generalization of the fitness model of trade, where the GDP plays the role of a
`macroeconomic fitness' shaping the binary and the weighted structure of the
ITN simultaneously. Our model mathematically highlights an important asymmetry
in the role of binary and weighted network properties, namely the fact that
binary properties can be inferred without the knowledge of weighted ones, while
the opposite is not true
A dynamic network model with persistent links and node-specific latent variables, with an application to the interbank market
We propose a dynamic network model where two mechanisms control the
probability of a link between two nodes: (i) the existence or absence of this
link in the past, and (ii) node-specific latent variables (dynamic fitnesses)
describing the propensity of each node to create links. Assuming a Markov
dynamics for both mechanisms, we propose an Expectation-Maximization algorithm
for model estimation and inference of the latent variables. The estimated
parameters and fitnesses can be used to forecast the presence of a link in the
future. We apply our methodology to the e-MID interbank network for which the
two linkage mechanisms are associated with two different trading behaviors in
the process of network formation, namely preferential trading and trading
driven by node-specific characteristics. The empirical results allow to
recognise preferential lending in the interbank market and indicate how a
method that does not account for time-varying network topologies tends to
overestimate preferential linkage.Comment: 19 pages, 6 figure
Randomizing bipartite networks: the case of the World Trade Web
Within the last fifteen years, network theory has been successfully applied
both to natural sciences and to socioeconomic disciplines. In particular,
bipartite networks have been recognized to provide a particularly insightful
representation of many systems, ranging from mutualistic networks in ecology to
trade networks in economy, whence the need of a pattern detection-oriented
analysis in order to identify statistically-significant structural properties.
Such an analysis rests upon the definition of suitable null models, i.e. upon
the choice of the portion of network structure to be preserved while
randomizing everything else. However, quite surprisingly, little work has been
done so far to define null models for real bipartite networks. The aim of the
present work is to fill this gap, extending a recently-proposed method to
randomize monopartite networks to bipartite networks. While the proposed
formalism is perfectly general, we apply our method to the binary, undirected,
bipartite representation of the World Trade Web, comparing the observed values
of a number of structural quantities of interest with the expected ones,
calculated via our randomization procedure. Interestingly, the behavior of the
World Trade Web in this new representation is strongly different from the
monopartite analogue, showing highly non-trivial patterns of self-organization.Comment: 22 pages, 13 figure
Systemic risk analysis in reconstructed economic and financial networks
We address a fundamental problem that is systematically encountered when
modeling complex systems: the limitedness of the information available. In the
case of economic and financial networks, privacy issues severely limit the
information that can be accessed and, as a consequence, the possibility of
correctly estimating the resilience of these systems to events such as
financial shocks, crises and cascade failures. Here we present an innovative
method to reconstruct the structure of such partially-accessible systems, based
on the knowledge of intrinsic node-specific properties and of the number of
connections of only a limited subset of nodes. This information is used to
calibrate an inference procedure based on fundamental concepts derived from
statistical physics, which allows to generate ensembles of directed weighted
networks intended to represent the real system, so that the real network
properties can be estimated with their average values within the ensemble. Here
we test the method both on synthetic and empirical networks, focusing on the
properties that are commonly used to measure systemic risk. Indeed, the method
shows a remarkable robustness with respect to the limitedness of the
information available, thus representing a valuable tool for gaining insights
on privacy-protected economic and financial systems
Enhanced reconstruction of weighted networks from strengths and degrees
Network 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 naive 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
Estimating topological properties of weighted networks from limited information
A problem typically encountered when studying complex systems is the limitedness of the information available on their topology, which hinders our understanding of their structure and of the dynamical processes taking place on them. A paramount example is provided by financial networks, whose data are privacy protected: Banks publicly disclose only their aggregate exposure towards other banks, keeping individual exposures towards each single bank secret. Yet, the estimation of systemic risk strongly depends on the detailed structure of the interbank network. The resulting challenge is that of using aggregate information to statistically reconstruct a network and correctly predict its higher-order properties. Standard approaches either generate unrealistically dense networks, or fail to reproduce the observed topology by assigning homogeneous link weights. Here, we develop a reconstruction method, based on statistical mechanics concepts, that makes use of the empirical link density in a highly nontrivial way. Technically, our approach consists in the preliminary estimation of node degrees from empirical node strengths and link density, followed by a maximum-entropy inference based on a combination of empirical strengths and estimated degrees. Our method is successfully tested on the international trade network and the interbank money market, and represents a valuable tool for gaining insights on privacy-protected or partially accessible systems
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