There is nowadays a constant flux of data being generated and collected in all types of real
world systems. These data sets are often indexed by time, space, or both requiring …

We use LASSO methods to shrink, select, and estimate the high‐dimensional network
linking the publicly traded subset of the world's top 150 banks, 2003–2014. We characterize …

In this paper we propose an empirical methodology to measure systemic risk. Building upon
Acharya et al.(2010), we think of the systemic risk of a financial institution as its contribution …

On the past, present, and future of the Diebold–Yilmaz approach to dynamic network
connectedness - ScienceDirect Skip to main contentSkip to article Elsevier logo Journals & Books …

Abstract Graph Convolutional Networks (GCNs) have shown promising results in modeling
graph-structured data. However, they have difficulty with processing digraphs because of …

Abstract Graph Contrastive Learning (GCL) has emerged to learn generalizable
representations from contrastive views. However, it is still in its infancy with two concerns: 1) …

This paper is concerned with an introduction to big data which can be potentially used in
nowcasting the UK GDP and other key macroeconomic variables. We discuss various big …

We study the behavior of the interbank market around the 2008 financial crisis. Using
network analysis, we study two network structures, correlation networks based on publicly …

An understanding of the spatial dimension of economic and social activity requires methods
that can separate out the relationship between spatial units that is due to the effect of …

In this study, a financial market is conceived as a network where the securities are nodes
and the links account for returns' correlations. We theoretically prove the negative …