Reconstructing parameters of spreading models from partial observations
This work addresses a key challenge in predicting and controlling diffusion processes on networks, which is incremental as it builds on existing spreading models but improves parameter estimation under realistic data constraints.
The paper tackles the problem of reconstructing transmission probabilities in spreading models from incomplete node activation data, introducing a dynamic message-passing algorithm that efficiently handles partial observations and generalizes to various dynamic models and temporal graphs.
Spreading processes are often modelled as a stochastic dynamics occurring on top of a given network with edge weights corresponding to the transmission probabilities. Knowledge of veracious transmission probabilities is essential for prediction, optimization, and control of diffusion dynamics. Unfortunately, in most cases the transmission rates are unknown and need to be reconstructed from the spreading data. Moreover, in realistic settings it is impossible to monitor the state of each node at every time, and thus the data is highly incomplete. We introduce an efficient dynamic message-passing algorithm, which is able to reconstruct parameters of the spreading model given only partial information on the activation times of nodes in the network. The method is generalizable to a large class of dynamic models, as well to the case of temporal graphs.