Showing papers by "Tiziano Squartini published in 2016"

Detecting early signs of the 2007–2008 crisis in the world trade

Abstract: Since 2007, several contributions have tried to identify early-warning signals of the financial crisis. However, the vast majority of analyses has focused on financial systems and little theoretical work has been done on the economic counterpart. In the present paper we fill this gap and employ the theoretical tools of network theory to shed light on the response of world trade to the financial crisis of 2007 and the economic recession of 2008–2009. We have explored the evolution of the bipartite World Trade Web (WTW) across the years 1995–2010, monitoring the behavior of the system both before and after 2007. Our analysis shows early structural changes in the WTW topology: since 2003, the WTW becomes increasingly compatible with the picture of a network where correlations between countries and products are progressively lost. Moreover, the WTW structural modification can be considered as concluded in 2010, after a seemingly stationary phase of three years. We have also refined our analysis by considering specific subsets of countries and products: the most statistically significant early-warning signals are provided by the most volatile macrosectors, especially when measured on developing countries, suggesting the emerging economies as being the most sensitive ones to the global economic cycles.

Inferring monopartite projections of bipartite networks: an entropy-based approach

Abstract: Bipartite networks are currently regarded as providing a major insight into the organization of many real-world systems, unveiling the mechanisms driving the interactions occurring between distinct groups of nodes. One of the most important issues encountered when modeling bipartite networks is devising a way to obtain a (monopartite) projection on the layer of interest, which preserves as much as possible the information encoded into the original bipartite structure. In the present paper we propose an algorithm to obtain statistically-validated projections of bipartite networks, according to which any two nodes sharing a statistically-significant number of neighbors are linked. Since assessing the statistical significance of nodes similarity requires a proper statistical benchmark, here we consider a set of four null models, defined within the exponential random graph framework. Our algorithm outputs a matrix of link-specific p-values, from which a validated projection is straightforwardly obtainable, upon running a multiple hypothesis testing procedure. Finally, we test our method on an economic network (i.e. the countries-products World Trade Web representation) and a social network (i.e. MovieLens, collecting the users' ratings of a list of movies). In both cases non-trivial communities are detected: while projecting the World Trade Web on the countries layer reveals modules of similarly-industrialized nations, projecting it on the products layer allows communities characterized by an increasing level of complexity to be detected; in the second case, projecting MovieLens on the films layer allows clusters of movies whose affinity cannot be fully accounted for by genre similarity to be individuated.

Hierarchical organization of functional connectivity in the mouse brain: a complex network approach.

Abstract: 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.

Multiplexity and multireciprocity in directed multiplexes.

Abstract: In 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, ie 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

Grandcanonical projection of bipartite networks.

Abstract: Fabio Saracco, 2 Riccardo Di Clemente, Andrea Gabrielli, 2 and Tiziano Squartini 2, ∗ IMT School for Advanced Studies, P.zza S. Ponziano 6, 55100 Lucca (Italy) Institute for Complex Systems (ISC-CNR) UOS Sapienza, “Sapienza” University of Rome, P.le A. Moro 5, 00185 Rome (Italy) Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Massachusetts Avenue 77, MA 02139 Cambridge (USA) (Dated: July 11, 2016)

Network reconstruction via density sampling

Abstract: Reconstructing weighted networks from partial information is necessary in many important circumstances, e.g. for a correct estimation of systemic risk. It has been shown that, in order to achieve an accurate reconstruction, it is crucial to reliably replicate the empirical degree sequence, which is however unknown in many realistic situations. More recently, it has been found that the knowledge of the degree sequence can be replaced by the knowledge of the strength sequence, which is typically accessible, complemented by that of the total number of links, thus considerably relaxing the observational requirements. Here we further relax these requirements and devise a procedure valid when even the the total number of links is unavailable. We assume that, apart from the heterogeneity induced by the degree sequence itself, the network is homogeneous, so that its (global) link density can be estimated by sampling subsets of nodes with representative density. We show that the best way of sampling nodes is the random selection scheme, any other procedure being biased towards unrealistically large, or small, link densities. We then introduce our core technique for reconstructing both the topology and the link weights of the unknown network in detail. When tested on real economic and financial data sets, our method achieves a remarkable accuracy and is very robust with respect to the sampled subsets, thus representing a reliable practical tool whenever the available topological information is restricted to small portions of nodes.

Detecting early signs of the 2007–2008 crisis in the world trade

Abstract: European Commission. Community Research and Development Information Service (Project GROWTHCOM 611272)

Hierarchical organization of functional connectivity in the mouse brain: a complex network approach

Abstract: 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.

Stock markets reconstruction via entropy maximization driven by fitness and density

Abstract: The spreading of financial distress in capital markets and the resulting systemic risk strongly depend on the detailed structure of financial interconnections. Yet, while financial institutions have to disclose their aggregated balance sheet data, the information on single positions is often unavailable due to privacy issues. The resulting challenge is that of using the aggregate information to statistically reconstruct financial networks and correctly predict their higher-order properties. However, standard approaches generate unrealistically dense networks, which severely underestimate systemic risk. Moreover, reconstruction techniques are generally cast for networks of bilateral exposures between financial institutions (such as the interbank market), whereas, the network of their investment portfolios (i.e., the stock market) has received much less attention. Here we develop an improved reconstruction method, based on statistical mechanics concepts and tailored for bipartite market networks. Technically, our approach consists in the preliminary estimation of connection probabilities by maximum-entropy inference driven by entities capitalizations and link density, followed by a density-corrected gravity model to assign position weights. Our method is successfully tested on NASDAQ, NYSE and AMEX filing data, by correctly reproducing the network topology and providing reliable estimates of systemic risk over the market.

Enhanced capital-asset pricing model for bipartite financial networks reconstruction

Abstract: Reconstructing patterns of interconnections from partial information is one of the most important issues in the statistical physics of complex networks. A paramount example is provided by financial networks. In fact, the spreading and amplification of financial distress in capital markets is strongly affected by the interconnections among financial institutions. Yet, while the aggregate balance sheets of these institutions are publicly disclosed, information on single positions is mostly confidential and, as such, unavailable. Standard approaches to reconstruct the network of financial interconnection produce unrealistically dense topologies, leading to a biased estimation of systemic risk. Moreover, reconstruction techniques are generally designed for monopartite networks of bilateral exposures between financial institutions, thus failing in reproducing bipartite networks of security holdings (e.g., investment portfolios). Here we propose a reconstruction method based on constrained entropy maximization, tailored for bipartite financial networks. Such a procedure enhances the traditional capital-asset pricing model (CAPM) and allows to reproduce the correct topology of the network. We test the method on a dataset, collected by the European Central Bank, of detailed security holdings of European institutional sectors over a period of six years (2009-2015). Our approach outperforms the traditional CAPM and the recently proposed MECAPM both in reproducing the network topology and in estimating systemic risk.

A generation-attraction model for renewable energy flows in Italy: A complex network approach

Abstract: In recent years, in Italy, the trend of the electricity demand and the need to connect a large number of renewable energy power generators to the power-grid, developed a novel type of energy transmission/distribution infrastructure. The Italian Transmission System Operator (TSO) and the Distribution System Operator (DSO), worked on a new infrastructural model, based on electronic meters and information technology. In pursuing this objective it is crucial importance to understand how even more larger shares of renewable energy can be fully integrated, providing a constant and reliable energy background over space and time. This is particularly true for intermittent sources as photovoltaic installations due to the fine-grained distribution of them across the Country. In this work we use an over-simplified model to characterize the Italian power grid as a graph whose nodes are Italian municipalities and the edges cross the administrative boundaries between a selected municipality and its first neighbours, following a Delaunay triangulation. Our aim is to describe the power flow as a diffusion process over a network, and using open data on the solar irradiation at the ground level, we estimate the production of photovoltaic energy in each node. An attraction index was also defined using demographic data, in accordance with average per capita energy consumption data. The available energy on each node was calculated by finding the stationary state of a generation-attraction model.

Detecting early signs of the 2007-2008 crisis in the world trade

Abstract: Since 2007, several contributions have tried to identify early-warning signals of the financial crisis. However, the vast majority of analyses has focused on financial systems and little theoretical work has been done on the economic counterpart. In the present paper we fill this gap and employ the theoretical tools of network theory to shed light on the response of world trade to the financial crisis of 2007 and the economic recession of 2008-2009. We have explored the evolution of the bipartite World Trade Web (WTW) across the years 1995-2010, monitoring the behavior of the system both before and after 2007. Our analysis shows early structural changes in the WTW topology: since 2003, the WTW becomes increasingly compatible with the picture of a network where correlations between countries and products are progressively lost. Moreover, the WTW structural modification can be considered as concluded in 2010, after a seemingly stationary phase of three years. We have also refined our analysis by considering specific subsets of countries and products: the most statistically significant early-warning signals are provided by the most volatile macrosectors, especially when measured on developing countries, suggesting the emerging economies as being the most sensitive ones to the global economic cycles.