Network science has evolved into an indispensable platform for studying complex systems.
But recent research has identified limits of classical networks, where links connect pairs of …

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 …

Complex systems, composed at the most basic level of units and their interactions, describe
phenomena in a wide variety of domains, from neuroscience to computer science and …

One of the main computational and scientific challenges in the modern age is to extract
useful information from unstructured texts. Topic models are one popular machine-learning …

We provide a short introduction to the field of topological data analysis (TDA) and discuss its
possible relevance for the study of complex systems. TDA provides a set of tools to …

Networks in nature have complex interactions among agents. One significant phenomenon
induced by interactions is synchronization of coupled agents, and the interactive network …

Latent Graph Inference (LGI) relaxed the reliance of Graph Neural Networks (GNNs) on a
given graph topology by dynamically learning it. However, most of LGI methods assume to …

Probabilistic Foundations of Statistical Network Analysis presents a fresh and insightful
perspective on the fundamental tenets and major challenges of modern network analysis. Its …

Network science provides very powerful tools for extracting information from interacting data.
Although recently the unsupervised detection of phases of matter using machine learning …

Node centrality measures are among the most commonly used analytical techniques for
networks. They have long helped analysts to identify “important” nodes that hold power in a …