Graph Neural Networks (GNNs) have been widely used to learn representations on graphs and tackle many real-world problems from a wide range of domains. In this paper we propose …
Networks are a powerful tool to model complex systems, and the definition of many Graph Neural Networks (GNN), Deep Learning algorithms that can handle networks, has opened a new …
Networks measurement is essential to catch and quantify their features, behaviour and/or emerging phenomena. The goal of cohesiveness metric introduced here is to establish the …
Combinatorial optimization problems arise in a wide range of applications from diverse domains. Many of these problems are NP-hard and designing efficient heuristics for them …
Basics of PageRank algorithm have been widely adopted in its variations, tailored for specific scenarios. In this work, we consider the Black Hole metric, an extension of the …
Choosing an effective yet efficient solution to the link building problem means to select which nodes in a network should point a newcomer in order to increase its rank while …
Many systems are today modelled as complex networks, since this representation has been proven being an effective approach for understanding and controlling many real-world …
We propose a multi-stage learning approach for pruning the search space of maximum clique enumeration, a fundamental computationally difficult problem arising in various network …
Ranking is a widely used technique to classify nodes in networks according to their relevance. Increasing one’s rank is a desiderable feature in almost any context; several …
Network fault tolerance (also known as resilience or robustness) is becoming a highly relevant topic, expecially in real networks, where it is essential to know to what extent it …