The complexity of many biological, social and technological systems stems from the richness
of the interactions among their units. Over the past decades, a variety of complex systems …

Graph machine learning (GML) is receiving growing interest within the pharmaceutical and
biotechnology industries for its ability to model biomolecular structures, the functional …

While Graph Neural Networks (GNNs) have achieved remarkable results in a variety of
applications, recent studies exposed important shortcomings in their ability to capture the …

Graphs arise naturally in many real-world applications including social networks,
recommender systems, ontologies, biology, and computational finance. Traditionally …

Networks are a fundamental tool for understanding and modeling complex systems in
physics, biology, neuroscience, engineering, and social science. Many networks are known …

Networks are one of the most common ways to represent biological systems as complex sets
of binary interactions or relations between different bioentities. In this article, we discuss the …

With the impressive growth of available data and the flexibility of network modelling, the
problem of devising effective quantitative methods for the comparison of networks arises …

Rapid technological advances have led to the production of different types of biological data
and enabled construction of complex networks with various types of interactions between …

Network medicine is an emerging area of research dealing with molecular and genetic
interactions, network biomarkers of disease, and therapeutic target discovery. Large-scale …

Motivation: Analogous to biological sequence comparison, comparing cellular networks is
an important problem that could provide insight into biological understanding and …