3,355 research outputs found
Null Models of Economic Networks: The Case of the World Trade Web
In all empirical-network studies, the observed properties of economic
networks are informative only if compared with a well-defined null model that
can quantitatively predict the behavior of such properties in constrained
graphs. However, predictions of the available null-model methods can be derived
analytically only under assumptions (e.g., sparseness of the network) that are
unrealistic for most economic networks like the World Trade Web (WTW). In this
paper we study the evolution of the WTW using a recently-proposed family of
null network models. The method allows to analytically obtain the expected
value of any network statistic across the ensemble of networks that preserve on
average some local properties, and are otherwise fully random. We compare
expected and observed properties of the WTW in the period 1950-2000, when
either the expected number of trade partners or total country trade is kept
fixed and equal to observed quantities. We show that, in the binary WTW,
node-degree sequences are sufficient to explain higher-order network properties
such as disassortativity and clustering-degree correlation, especially in the
last part of the sample. Conversely, in the weighted WTW, the observed sequence
of total country imports and exports are not sufficient to predict higher-order
patterns of the WTW. We discuss some important implications of these findings
for international-trade models.Comment: 39 pages, 46 figures, 2 table
Null Models of Economic Networks: The Case of the World Trade Web
In all empirical-network studies, the observed properties of economic networks are informative only if compared with a well-defined null model that can quantitatively predict the behavior of such properties in constrained graphs. However, predictions of the available null-model methods can be derived analytically only under assumptions (e.g., sparseness of the network) that are unrealistic for most economic networks like the World Trade Web (WTW). In this paper we study the evolution of the WTW using a recently-proposed family of null network models. The method allows to analytically obtain the expected value of any network statistic across the ensemble of networks that preserve on average some local properties, and are otherwise fully random. We compare expected and observed properties of the WTW in the period 1950-2000, when either the expected number of trade partners or total country trade is kept fixed and equal to observed quantities. We show that, in the binary WTW, node-degree sequences are sufficient to explain higher-order network properties such as disassortativity and clustering-degree correlation, especially in the last part of the sample. Conversely, in the weighted WTW, the observed sequence of total country imports and exports are not sufficient to predict higher-order patterns of the WTW. We discuss some important implications of these findings for international-trade models.World Trade Web; Null Models of Networks; Complex Networks; International Trade
The multiplex structure of interbank networks
The interbank market has a natural multiplex network representation. We
employ a unique database of supervisory reports of Italian banks to the Banca
d'Italia that includes all bilateral exposures broken down by maturity and by
the secured and unsecured nature of the contract. We find that layers have
different topological properties and persistence over time. The presence of a
link in a layer is not a good predictor of the presence of the same link in
other layers. Maximum entropy models reveal different unexpected substructures,
such as network motifs, in different layers. Using the total interbank network
or focusing on a specific layer as representative of the other layers provides
a poor representation of interlinkages in the interbank market and could lead
to biased estimation of systemic risk.Comment: 41 pages, 8 figures, 10 table
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EEG-Based Quantification of Cortical Current Density and Dynamic Causal Connectivity Generalized across Subjects Performing BCI-Monitored Cognitive Tasks.
Quantification of dynamic causal interactions among brain regions constitutes an important component of conducting research and developing applications in experimental and translational neuroscience. Furthermore, cortical networks with dynamic causal connectivity in brain-computer interface (BCI) applications offer a more comprehensive view of brain states implicated in behavior than do individual brain regions. However, models of cortical network dynamics are difficult to generalize across subjects because current electroencephalography (EEG) signal analysis techniques are limited in their ability to reliably localize sources across subjects. We propose an algorithmic and computational framework for identifying cortical networks across subjects in which dynamic causal connectivity is modeled among user-selected cortical regions of interest (ROIs). We demonstrate the strength of the proposed framework using a "reach/saccade to spatial target" cognitive task performed by 10 right-handed individuals. Modeling of causal cortical interactions was accomplished through measurement of cortical activity using (EEG), application of independent component clustering to identify cortical ROIs as network nodes, estimation of cortical current density using cortically constrained low resolution electromagnetic brain tomography (cLORETA), multivariate autoregressive (MVAR) modeling of representative cortical activity signals from each ROI, and quantification of the dynamic causal interaction among the identified ROIs using the Short-time direct Directed Transfer function (SdDTF). The resulting cortical network and the computed causal dynamics among its nodes exhibited physiologically plausible behavior, consistent with past results reported in the literature. This physiological plausibility of the results strengthens the framework's applicability in reliably capturing complex brain functionality, which is required by applications, such as diagnostics and BCI
Hierarchical organization of functional connectivity in the mouse brain: a complex network approach
This paper represents a contribution to the study of the brain functional
connectivity from the perspective of complex networks theory. More
specifically, we apply graph theoretical analyses to provide evidence of the
modular structure of the mouse brain and to shed light on its hierarchical
organization. We propose a novel percolation analysis and we apply our approach
to the analysis of a resting-state functional MRI data set from 41 mice. This
approach reveals a robust hierarchical structure of modules persistent across
different subjects. Importantly, we test this approach against a statistical
benchmark (or null model) which constrains only the distributions of empirical
correlations. Our results unambiguously show that the hierarchical character of
the mouse brain modular structure is not trivially encoded into this
lower-order constraint. Finally, we investigate the modular structure of the
mouse brain by computing the Minimal Spanning Forest, a technique that
identifies subnetworks characterized by the strongest internal correlations.
This approach represents a faster alternative to other community detection
methods and provides a means to rank modules on the basis of the strength of
their internal edges.Comment: 11 pages, 9 figure
Rewiring World Trade. Part I: A Binary Network Analysis
The international trade network (ITN) has received renewed multidisciplinary interest due to recent advances in network theory. However, it is still unclear whether a network approach conveys additional, nontrivial information with respect to traditional international-economics analyses that describe world trade only in terms of local (rst-order) properties. In this and in a companion paper, we employ a recently-proposed randomization method to assess in detail the role that local properties have in shaping higher-order patterns of the ITN in all its possible representations (binary/ weighted, directed/undirected, aggregated/disaggregated) and across several years. Here we show that, remarkably, all the properties of all binary projections of the network can be completely traced back to the degree sequence, which is therefore maximally informative. Our results imply that explaining the observed degree sequence of the ITN, which has not received particular attention in economic theory, should instead become one the main focuses of models of trade.
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