The propagations of diseases, behaviors and information in real systems are rarely
independent of each other, but they are coevolving with strong interactions. To uncover the …

We study reinforcement learning (RL) in a setting with a network of agents whose states and
actions interact in a local manner where the objective is to find localized policies such that …

The cavity method is one of the cornerstones of the statistical physics of disordered systems
such as spin glasses and other complex systems. It is able to analytically and asymptotically …

Discrete dynamical systems can exhibit complex behavior from the iterative application of
straightforward local rules. A famous class of examples comes from cellular automata whose …

Contact tracing is an essential tool to mitigate the impact of a pandemic, such as the COVID-
19 pandemic. In order to achieve efficient and scalable contact tracing in real time, digital …

The effective use of limited resources for controlling spreading processes on networks is of
prime significance in diverse contexts, ranging from the identification of “influential …

Researchers have developed several theoretical methods for predicting epidemic
thresholds, including the mean-field like (MFL) method, the quenched mean-field (QMF) …

Epidemic processes are common out-of-equilibrium phenomena of broad interdisciplinary
interest. Recently, dynamic message-passing (DMP) has been proposed as an efficient …

In this work we extend the SEIR model as in epidemic disease modeling to investigate the
propagation dynamics of the information online. Here, we go one step further and takes the …

Source inference aims at revealing the seed of the misinformation spreading on social
networks, and attracted great attention in the field of network science and cybersecurity …