activity
20172026
most citedMobile phone data and COVID-19: Missing an opportunity?

106 citations · 514 across the 39 of their papers we have counts for

collaborators
Showing 2023Show all

7 papers · 1 filter

cs.CL2023★ 2 cited

Glitter or Gold? Deriving Structured Insights from Sustainability Reports via Large Language Models

Marco Bronzini, Carlo Nicolini, Bruno Lepri +2

Over the last decade, several regulatory bodies have started requiring the disclosure of non-financial information from publicly listed companies, in light of the investors' increa…

physics.soc-ph2023

Adaptation of Student Behavioural Routines during COVID-19: A Multimodal Approach

Nicolò A. Girardini, Simone Centellegher, Andrea Passerini +3

One population group that had to significantly adapt and change their behaviour during the COVID-19 pandemic is students. While previous studies have extensively investigated the i…

cs.CV2023★ 1 cited

T2TD: Text-3D Generation Model based on Prior Knowledge Guidance

Weizhi Nie, Ruidong Chen, Weijie Wang +2

In recent years, 3D models have been utilized in many applications, such as auto-driver, 3D reconstruction, VR, and AR. However, the scarcity of 3D model data does not meet its pra…

physics.soc-ph2023★ 9 cited

Socioeconomic disparities in mobility behavior during the COVID-19 pandemic in developing countries

Lorenzo Lucchini, Ollin Langle-Chimal, Lorenzo Candeago +6

Mobile phone data have played a key role in quantifying human mobility during the COVID-19 pandemic. Existing studies on mobility patterns have primarily focused on regional aggreg…

physics.soc-ph2023

Analysis of COVID-19 first wave in the US based on demographic, mobility, and environmental variables

Dario Spiller, Gabriele Santin, Alessandro Sebastianelli +6

COVID-19 had a strong and disruptive impact on our society, and yet further analyses on most relevant factors explaining the spread of the pandemic are needed. Interdisciplinary st…

cs.LG2023★ 33 cited

Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities

Antonio Longa, Veronica Lachi, Gabriele Santin +5

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 grap…