activity
20172026
most citedModeling spatio-temporal locality in multi-step forecasting of geo-referenced time series

5 citations · 13 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2026★ 5 cited

Modeling spatio-temporal locality in multi-step forecasting of geo-referenced time series

Annunziata D'Aversa, Gianvito Pio, Michelangelo Ceci

Forecasting future measurements from geographically distributed sensors is essential across many domains. However, the spatial distribution of these sensors raises multiple challen…

cs.SI2026★ 5 cited

IMMENSE: Inductive Multi-perspective User Classification in Social Networks

Francesco Benedetti, Antonio Pellicani, Gianvito Pio +1

Online social networks increasingly expose people to users who propagate discriminatory, hateful, and violent content. Young users, in particular, are vulnerable to exposure to suc…

cs.CR2026

DNS-GT: A Graph-based Transformer Approach to Learn Embeddings of Domain Names from DNS Queries

Massimiliano Altieri, Ronan Hamon, Roberto Corizzo +2

Network intrusion detection systems play a crucial role in the security strategy employed by organisations to detect and prevent cyberattacks. Such systems usually combine pattern…

cs.SI2025

Multi-view Learning for the Identification of Risky Users in Dynamic Social Networks

Francesco Benedetti, Antonio Pellicani, Gianvito Pio +1

Technological progress in the last few decades has granted an increasing number of people access to social media platforms such as Facebook, X (formerly Twitter), and Instagram. Co…

cs.LG2022★ 2 cited

Semi-supervised Predictive Clustering Trees for (Hierarchical) Multi-label Classification

Jurica Levatić, Michelangelo Ceci, Dragi Kocev +1

Semi-supervised learning (SSL) is a common approach to learning predictive models using not only labeled examples, but also unlabeled examples. While SSL for the simple tasks of cl…

cs.AI2017★ 1 cited

Temporal Pattern Mining from Evolving Networks

Angelo Impedovo, Corrado Loglisci, Michelangelo Ceci

Recently, evolving networks are becoming a suitable form to model many real-world complex systems, due to their peculiarities to represent the systems and their constituting entiti…