The application of Artificial Intelligence (AI) to financial investment is a research area that
has attracted extensive research attention since the 1990s, when there was an accelerated …

Graph-based approaches are revolutionizing the analysis of different real-life systems, and
the stock market is no exception. Individual stocks and stock market indices are connected …

The manipulation of neutral atoms by light is at the heart of countless scientific discoveries in
the field of quantum physics in the last three decades. The level of control that has been …

Combinatorial optimization problems are pervasive across science and industry. Modern
deep learning tools are poised to solve these problems at unprecedented scales, but a …

This paper proposes a novel interconnected multilayer network framework based on
variance decomposition and block aggregation technique, which can be further served as a …

Inferring large-scale covariance matrices from sparse genomic data is an ubiquitous
problem in bioinformatics. Clearly, the widely used standard covariance and correlation …

Using the CAViaR tool to estimate the value-at-risk (VaR) and the Granger causality risk test
to quantify extreme risk spillovers, we propose an extreme risk spillover network for …

We construct a Pearson correlation-based network and a partial correlation-based network,
ie, two minimum spanning trees (MST-Pearson and MST-Partial), to analyze the correlation …

Statistical network modelling has focused on representing the graph as a discrete structure,
namely the adjacency matrix. When assuming exchangeability of this array—which can aid …

In many practical important cases, a massive dataset can be represented as a very large
network with certain attributes associated with its vertices and edges. Stock markets …