We review the state of the art of clustering financial time series and the study of their
correlations alongside other interaction networks. The aim of the review is to gather in one …

Network representations are powerful tools to modeling the dynamic time-varying financial
complex systems consisting of multiple co-evolving financial time series, eg, stock prices. In …

We develop a novel method for measuring the similarity between complete weighted
graphs, which are probed by means of the discrete-time quantum walks. Directly probing …

A financial market is a complex system consisting of many interacting units. In general, due
to the various types of information exchange within the industry, there is a relationship …

The structure of networks can be efficiently represented using motifs, which are those
subgraphs that recur most frequently. One route to understanding the motif structure of a …

This article explores the thermodynamic characterization of networks using the heat bath
analogy when the energy states are occupied under different spin statistics, specified by a …

This study proposes a framework to diagnose stock market crashes and predict the
subsequent price rebounds. Based on the observation of anomalous changes in stock …

In this paper, we present a novel thermodynamically based analysis method for directed
networks, and in particular for time-evolving networks in the finance domain. Based on an …

Financial markets are time-evolving complex systems containing different financial entities,
such as banks, corporations and institutions that interact through transactions and respond …

In recent years, kernel methods are widespread in tasks of similarity measuring. Specifically,
graph kernels are widely used in fields of bioinformatics, chemistry and financial data …