Ricerc@Sapienza

Multidimensional network data can have different levels of complexity, as nodes may be characterized by heterogeneous individual-specific features, which may vary across the networks. This article introduces a class of models for multidimensional network data, where different levels of heterogeneity within and between networks can be considered. The proposed framework is developed in the family of latent space models, and it aims to distinguish symmetric relations between the nodes and node-specific features. Model parameters are estimated via a Markov Chain Monte Carlo algorithm.

During the past three decades, due to the development of new technologies that allow faster and more accurate virological diagnoses, clinical virology laboratory has taken on an important role in the patient’s clinical management. Besides, it is now possible to define, more quickly and precisely than before, viral gene sequences related to specific viral variants (including drug-resistant strains) or to viral and host factors that can affect the natural history of infections.