Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static)
graph-structured data. However, many real-world systems are dynamic in nature, since the …

Complex networks pervade various real-world systems, from the natural environment to
human societies. The essence of these networks is in their ability to transition and evolve …

In many domains, effectively applying machine learning models requires a large number of
annotations and labelled data, which might not be available in advance. Acquiring …

Most machine learning and data analytics applications, including performance engineering
in software systems, require a large number of annotations and labelled data, which might …

Anticipating audience reaction towards a certain piece of text is integral to several facets of
society ranging from politics, research, and commercial industries. Sentiment analysis (SA) …

High-resolution temporal data on contacts between hosts provide crucial information on the
mixing patterns underlying infectious disease transmission. Publicly available data sets of …

Predicting the performance of production code prior to actual execution is known to be
highly challenging. In this paper, we propose a predictive model, dubbed TEP-GNN, which …

Temporal networks of face-to-face interactions between individuals are useful proxies of the
dynamics of social systems on fast timescales. Several empirical statistical properties of …

The analysis of complex and time-evolving interactions, such as those within social
dynamics, represents a current challenge in the science of complex systems. Temporal …

Surrogate networks can constitute suitable replacements for real networks, in particular to
study dynamical processes on networks, when only incomplete or limited datasets are …