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

The physics of spreading processes in multilayer networks

The study of networks plays a crucial role in investigating the structure, dynamics, and function of a wide variety of complex systems in myriad disciplines. Despite the success of traditional network analysis, standard networks provide a limited representation of complex systems, which often include different types of relationships (i.e., "multiplexity") among their constituent components and/or multiple interacting subsystems. Such structural complexity has a significant effect on both dynamics and function. Throwing away or aggregating available structural information can generate misleading results and be a major obstacle towards attempts to understand complex systems. The recent "multilayer" approach for modeling networked systems explicitly allows the incorporation of multiplexity and other features of realistic systems. On one hand, it allows one to couple different structural relationships by encoding them in a convenient mathematical object. On the other hand, it also allows one to couple different dynamical processes on top of such interconnected structures. The resulting framework plays a crucial role in helping achieve a thorough, accurate understanding of complex systems. The study of multilayer networks has also revealed new physical phenomena that remain hidden when using ordinary graphs, the traditional network representation. Here we survey progress towards attaining a deeper understanding of spreading processes on multilayer networks, and we highlight some of the physical phenomena related to spreading processes that emerge from multilayer structure.Comment: 25 pages, 4 figure

Econometric Information Recovery in Behavioral Networks

In this paper, we suggest an approach to recovering behavior-related, preference-choice network information from observational data. We model the process as a self-organized behavior based random exponential network-graph system. To address the unknown nature of the sampling model in recovering behavior related network information, we use the Cressie-Read (CR) family of divergence measures and the corresponding information theoretic entropy basis, for estimation, inference, model evaluation, and prediction. Examples are included to clarify how entropy based information theoretic methods are directly applicable to recovering the behavioral network probabilities in this fundamentally underdetermined ill posed inverse recovery problem

Interbank borrowing and lending between financially constrained banks

Some stylized facts about transactions among banks are not easily reconciled with coinsurance of short-term liquidity risks. In our model, interbank markets play a different role. We argue that lending to another bank can reduce a bank’s overall portfolio risk through diversification. If insolvency is costly, this diversification improves the interbank lender's funding liquidity, boosting credit supply to nonbanks. However, diversification comes at an endogenous cost that depends on bank-specific factors of interbank borrower and lender. The model provides a framework for understanding the importance of interbank lending for aggregate credit supply and the stability of banking systems. The model’s predictions are consistent with evidence documented in the literature that other theories cannot consistently explain

Open letter to Senator Rita Levi-Montalcini.

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Multiplex financial networks: revealing the level of interconnectedness in the banking system

Complex networks models have been useful for the study of systemic risk; however, most of the studies have ignored the true level of interconnectedness in the financial system; in this work we show the missing part on the study of interconnectedness. Furthermore, complexity in modern financial systems has been also an important subject of study. However, we still lack the appropriate metrics to describe such complexity and interconnectedness; moreover, the data available in order to describe and study them is still scarce. In addition, most of the focus on the subject of interconnectedness has been on a single type of network: interbank (exposures) networks. We use a comprehensive set of market interactions that include transactions in the securities markets, payment system flows, interbank loans, cross holding of securities, foreign exchange exposures and derivatives exposures. This the first attempt, to the best of our knowledge, to describe so comprehensively the complexity and interconnectedness in a banking system. We are able to identify the most important institutions in the whole structure in term of their connectedness, the most relevant layer (in structural terms) of the multiplex and the community structure of the Mexican banking system which can be seen as a generalization of the well-known Core-Periphery model