The main field of research I am interested in concerns Multidimensional (and Evolutive) Complex Network Analysis.
Traditionally, Complex Network Analysis has been monodimensional: researchers focused their attention to networks with a single kind of relation represented. KDDLab is pushing the research over multidimensional networks, i.e. network with multiple kind of relations, since they are a better model to represent the complexity in reality (transportation, infrastructure and social networks are often multidimensional). In this novel scenario, we want to develop a basic theory and simple analytical measures, as well as more complex analysis and algorithms, such as community discovery, link prediction and the analysis of highly connected nodes (i.e. hubs).
From the main multidimensional network research, different branches have emerged: we are investigating also mobility networks, using human trajectories as edges to connect different geographical areas and/or points of interest. Also, we have interest in analyze trust networks, particularly the problem of identifying possible privacy and/or security fallacies.
The main focus of my research is related to the temporal analysis of complex networks: discover how new links arise and how communities evolve over time could be very useful to understand, model and make predictions on the interactions that occur in a large variety of real networks (i.e. social networks, mobility networks).
- Link Prediction on Multidimensional and Temporal Networks
- Node Ranking in Multidimensional and Skills-annotated networks (UBIK)
- Multidimensional Strength of Ties
- Democratic Estimate of the Modular Organization of a Network (DEMON)
- Evolutionary Community Discovery
- Diffusion processes in online social networks
- Twitter\Foursquare\Gowalla\Flickr\Last.fm data social analysis
- GPS & Annotated Diaries reconciliation