327 citations · 1.2k across the 57 of their papers we have counts for
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Learning of networked spreading models from noisy and incomplete data
Mateusz Wilinski, Andrey Y. Lokhov
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…
Prediction-Centric Learning of Independent Cascade Dynamics from Partial Observations
Mateusz Wilinski, Andrey Y. Lokhov
Spreading processes play an increasingly important role in modeling for diffusion networks, information propagation, marketing and opinion setting. We address the problem of learni…
Scalable Influence Estimation Without Sampling
Andrey Y. Lokhov, David Saad
In a diffusion process on a network, how many nodes are expected to be influenced by a set of initial spreaders? This natural problem, often referred to as influence estimation, bo…
Reconstructing parameters of spreading models from partial observations
Andrey Y. Lokhov
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…
Optimal Deployment of Resources for Maximizing Impact in Spreading Processes
Andrey Y. Lokhov, David Saad
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…