The convolution operator at the core of many modern neural architectures can effectively be
seen as performing a dot product between an input matrix and a filter. While this is readily …

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 …

In this paper, we investigated the possibility of using the magnetic Laplacian to characterize
directed networks. We address the problem of characterization of network models and …

Feature selection is a crucial preprocessing step in data analysis and machine learning.
Since causal relationships imply the underlying mechanism of a system, causality-based …

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 …

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 …