Modern vehicles rely on a myriad of electronic control units (ECUs) interconnected via
controller area networks (CANs) for critical operations. Despite their ubiquitous use and …

Graphs are ubiquitous for modeling complex systems involving structured data and
relationships. Consequently, graph representation learning, which aims to automatically …

The ever-increasing importance of structured data in different applications, especially in the
biomedical field, has driven the need for reducing its complexity through projections into a …

As the number of graph-level embedding techniques increases at an unprecedented speed,
questions arise about their behavior and performance when training data undergo …

We consider the problem of causal structure learning in the setting of heterogeneous
populations, ie, populations in which a single causal structure does not adequately …

Networks provide a suitable model for many scientific and technological problems that
require the representation of complex entities and their relations. Life sciences applications …

In this paper we present a method to examine the synchrony between brains without the
need to carry out simultaneous recordings of EEG signals from two people which is the …

Essential genes are reductively defined as those fundamental for an organism's
reproductive success and growth. Still, the so-called essentiality of a gene is a context …

Graph representation has gained wide popularity as a data representation method in many
applications. Graph embedding methods convert graphs to a vector representation and are …

Graph embedding techniques are becoming increasingly common in many fields ranging
from scientific computing to biomedical applications and finance. These techniques aim to …