In the past 15 years, statistical physics has been successful as a framework for modelling
complex networks. On the theoretical side, this approach has unveiled a variety of physical …

Recent progress in applying complex network theory to problems in quantum information
has resulted in a beneficial cross-over. Complex network methods have successfully been …

Many complex systems can be represented as networks consisting of distinct types of
interactions, which can be categorized as links belonging to different layers. For example, a …

A network representation is useful for describing the structure of a large variety of complex
systems. However, most real and engineered systems have multiple subsystems and layers …

Multilayer relationships among entities and information about entities must be accompanied
by the means to analyse, visualize and obtain insights from such data. We present open …

In contrastive learning, the choice of" view" controls the information that the representation
captures and influences the performance of the model. However, leading graph contrastive …

The quantification of the complexity of networks is, today, a fundamental problem in the
physics of complex systems. A possible roadmap to solve the problem is via extending key …

Typical brain networks consist of many peripheral regions and a few highly central ones, ie,
hubs, playing key functional roles in cerebral inter-regional interactions. Studies have shown …

In 1953, Shannon proposed the question of quantification of structural information to analyze
communication systems. The question has become one of the longest great challenges in …

Network theory has unveiled the underlying structure of complex systems such as the
Internet or the biological networks in the cell. It has identified universal properties of complex …