Network Science

Machine learning dismantling and early-warning signals of disintegration in complex systems

From physics to engineering, biology and social science, natural and artificial systems are characterized by interconnected topologies whose features – e.g., heterogeneous …

wsGAT: Weighted and Signed Graph Attention Networks for Link Prediction

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 …

Relations Between Entropy and Accuracy Trends in Complex Artificial Neural Networks

mGNN: Generalizing the Graph Neural Networks to the Multilayer Case

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 …

Food Recommendation in a Worksite Canteen.

A Network-Based Analysis of a Worksite Canteen Dataset

The provision of wellness in workplaces gained interest in recent decades. A factor that contributes significantly to workers’ health is their diet, especially when provided by …

Group Cohesion Assessment in Networks

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 …

Learning fine-grained search space pruning and heuristics for combinatorial optimization

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 …

A network-based analysis to understand food-habits of a multi-company canteen's customers

A PageRank Inspired Approach to Measure Network Cohesiveness

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 …