Studying information diffusion in SNS (Social Networks Service) has remarkable
significance in both academia and industry. Theoretically, it boosts the development of other …

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 …

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

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 …

To infer a diffusion network based on observations from historical diffusion processes,
existing approaches assume that observation data contain exact occurrence time of each …

In a diffusion network, some nodes exhibit similar diffusion patterns as they have analogous
influence reachabilities to the other nodes. When these nodes are selected as initially …

The topology of diffusion networks plays an essential role in understanding information
propagation dynamics and conducting social network analysis. However, diffusion networks …

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 …

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 …

Numerous algorithms have been proposed to infer the underlying structure of the social
networks via observed information propagation. The previously proposed algorithms …