Learning of networked spreading models from noisy and incomplete data
This addresses challenges in model selection and efficient learning for spreading models, which is incremental as it builds on existing algorithms to handle real-world data issues like noise and missing observations.
The authors tackled the problem of learning parameters of spreading dynamics from noisy and incomplete data, introducing a universal method based on dynamic message-passing that reconstructs network structure and model parameters with linear computational complexity, making it scalable to large networks.
Recent years have seen a lot of progress in algorithms for learning parameters of spreading dynamics from both full and partial data. Some of the remaining challenges include model selection under the scenarios of unknown network structure, noisy data, missing observations in time, as well as an efficient incorporation of prior information to minimize the number of samples required for an accurate learning. Here, we introduce a universal learning method based on scalable dynamic message-passing technique that addresses these challenges often encountered in real data. The algorithm leverages available prior knowledge on the model and on the data, and reconstructs both network structure and parameters of a spreading model. We show that a linear computational complexity of the method with the key model parameters makes the algorithm scalable to large network instances.