Reconstructing parameters of spreading models from partial observations

Abstract

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.

Accepted to NIPS 2016

References in corpus (7)

• Epidemic processes in complex networks
• A message passing approach for general epidemic models
• Bayesian inference of epidemics on networks via Belief Propagation
• Dynamic message-passing equations for models with unidirectional dynamics
• Trace Complexity of Network Inference
• Reconstructing a Graph from Path Traces
• Efficient reconstruction of transmission probabilities in a spreading process from partial observations

Cited by in corpus (5)

• Optimal Deployment of Resources for Maximizing Impact in Spreading Processes
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• Network Diffusions via Neural Mean-Field Dynamics
• Discovering hidden layers in quantum graphs
• Efficient message passing for cascade size distributions on finite trees