Competition and collaboration are at the heart of multiagent probabilistic spreading
processes. The battle for public opinion and competitive marketing campaigns are typical …

To reconstruct the topology of a diffusion network, existing approaches customarily demand
not only eventual infection statuses of nodes, but also the exact times when infections occur …

Understanding complex systems requires understanding interactions between different
domains and different scales. Pandemic science serves as an exemplar of such complex …

Spreading processes play an increasingly important role in modeling for diffusion networks,
information propagation, marketing and opinion setting. We address the problem of learning …

Predicting stochastic spreading processes on complex networks is critical in epidemic
control, opinion propagation, and viral marketing. We focus on the problem of inferring the …

To learn influence relationships between nodes in a diffusion network, most existing
approaches resort to precise timestamps of historical node infections. The target network is …

Discrete dynamical systems are commonly used to model the spread of contagions on real-
world networks. Under the PAC framework, existing research has studied the problem of …

To infer the structure of a diffusion network from observed diffusion results, existing
approaches customarily assume that observed data are complete and contain the final …

Networked dynamical systems are widely used as formal models of real-world cascading
phenomena, such as the spread of diseases and information. Prior research has addressed …

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 …