Our focus is on realistically modeling and forecasting dynamic networks of face-to-face
contacts among individuals. Important aspects of such data that lead to problems with …

We present a Bayesian factorization model for discovering dynamic communities of actors in
a social network and for describing action types associated with those communities. The …

Evolving multiplex networks are a powerful model for representing the dynamics along time
of different phenomena, such as social networks, power grids, biological pathways …

Network data representing relationship structures among a set of nodes are available in
many fields of applications covering social science, neuroscience, business intelligence and …