International recessions

The 2007–2009 crisis was characterized by an unprecedented degree of international synchronization as all major industrialized countries experienced large macroeconomic contractions around the date of Lehman bankruptcy. At the same time countries also experienced large and synchronized tightening of credit conditions. We present a two-country model with financial market frictions where a credit tightening can emerge as a self-fulfilling equilibrium caused by pessimistic but fully rational expectations. As a result of the credit tightening, countries experience large and endogenously synchronized declines in asset prices and economic activity (international recessions). The model suggests that these recessions are more severe if they happen after a prolonged period of credit expansion.

International Recessions

The 2008-2009 crisis was characterized by an unprecedented degree of international synchronization as all major industrialized countries experienced large macroeconomic contractions. Countries also experienced large and synchronized contractions in the growth of financial flows. In this paper we present a two-country model with financial markets frictions where credit-driven recessions can explain these features of the recent crisis. A credit contraction can emerge as a self-fulfilling equilibrium caused by pessimistic but fully rational expectations. As a result of the credit contraction, in a financially integrated world, countries experience large and, endogenously synchronized, declines in asset prices and economic activity ( international recessions).

De Bruijn goes Neural: Causality-Aware Graph Neural Networks for Time Series Data on Dynamic Graphs

We introduce De Bruijn Graph Neural Networks (DBGNNs), a novel time-aware graph neural network architecture for time-resolved data on dynamic graphs. Our approach accounts for temporal-topological patterns that unfold in the causal topology of dynamic graphs, which is determined by causal walks, i.e. temporally ordered sequences of links by which nodes can influence each other over time. Our architecture builds on multiple layers of higher-order De Bruijn graphs, an iterative line graph construction where nodes in a De Bruijn graph of order k represent walks of length k-1, while edges represent walks of length k. We develop a graph neural network architecture that utilizes De Bruijn graphs to implement a message passing scheme that follows a non-Markovian dynamics, which enables us to learn patterns in the causal topology of a dynamic graph. Addressing the issue that De Bruijn graphs with different orders k can be used to model the same data set, we further apply statistical model selection to determine the optimal graph topology to be used for message passing. An evaluation in synthetic and empirical data sets suggests that DBGNNs can leverage temporal patterns in dynamic graphs, which substantially improves the performance in a supervised node classification task

Bayesian Inference of Transition Matrices from Incomplete Graph Data with a Topological Prior

Many network analysis and graph learning techniques are based on models of random walks which require to infer transition matrices that formalize the underlying stochastic process in an observed graph. For weighted graphs, it is common to estimate the entries of such transition matrices based on the relative weights of edges. However, we are often confronted with incomplete data, which turns the construction of the transition matrix based on a weighted graph into an inference problem. Moreover, we often have access to additional information, which capture topological constraints of the system, i.e. which edges in a weighted graph are (theoretically) possible and which are not, e.g. transportation networks, where we have access to passenger trajectories as well as the physical topology of connections, or a set of social interactions with the underlying social structure. Combining these two different sources of information to infer transition matrices is an open challenge, with implications on the downstream network analysis tasks. Addressing this issue, we show that including knowledge on such topological constraints can improve the inference of transition matrices, especially for small datasets. We derive an analytically tractable Bayesian method that uses repeated interactions and a topological prior to infer transition matrices data-efficiently. We compare it against commonly used frequentist and Bayesian approaches both in synthetic and real-world datasets, and we find that it recovers the transition probabilities with higher accuracy and that it is robust even in cases when the knowledge of the topological constraint is partial. Lastly, we show that this higher accuracy improves the results for downstream network analysis tasks like cluster detection and node ranking, which highlights the practical relevance of our method for analyses of various networked systems.Comment: 22 pages, 13 figure

Higher-Order Visualization of Causal Structures in Dynamics Graphs

Graph or network representations are an important foundation for data mining and machine learning tasks in relational data. Many tools of network analysis, like centrality measures, information ranking, or cluster detection rest on the assumption that links capture direct influence, and that paths represent possible indirect influence. This assumption is invalidated in time-stamped network data capturing, e.g., dynamic social networks, biological sequences or financial transactions. In such data, for two time-stamped links (A,B) and (B,C) the chronological ordering and timing determines whether a causal path from node A via B to C exists. A number of works has shown that for that reason network analysis cannot be directly applied to time-stamped network data. Existing methods to address this issue require statistics on causal paths, which is computationally challenging for big data sets. Addressing this problem, we develop an efficient algorithm to count causal paths in time-stamped network data. Applying it to empirical data, we show that our method is more efficient than a baseline method implemented in an OpenSource data analytics package. Our method works efficiently for different values of the maximum time difference between consecutive links of a causal path and supports streaming scenarios. With it, we are closing a gap that hinders an efficient analysis of big time series data on complex networks

Where Does Fluid-like Turbulence Break Down in the Solar Wind?

Power spectra of the magnetic field in solar wind display a Kolmogorov law f –5/3 at intermediate range of frequencies f, say within the inertial range. Two spectral breaks are also observed: one separating the inertial range from an f –1 spectrum at lower frequencies, and another one between the inertial range and an f –7/3 spectrum at higher frequencies. The breaking of fluid-like turbulence at high frequencies has been attributed to either the occurrence of kinetic Alfven wave fluctuations above the ion-cyclotron frequency or to whistler turbulence above the frequency corresponding to the proton gyroradius. Using solar wind data, we show that the observed high-frequency spectral break seems to be independent of the distance from the Sun, and then of both the ion-cyclotron frequency and the proton gyroradius. We suppose that the observed high-frequency break could be either caused by a combination of different physical processes or associated with a remnant signature of coronal turbulence