Learning of the information diffusion model is a fundamental problem in the study of information diffusion in social networks. Existing approaches learn the diffusion models from events in social networks. However, events in social networks may have different underlying reasons. Some of them may be caused by the social influence inside the network, while others may reflect external trends in the "real world". Most existing work on the learning of diffusion models does not distinguish the events caused by the social influence from those caused by external trends. In this paper, we extract social events from data streams in social networks, and then use the extracted social events to improve the learning of information diffusion models. We propose a LADP (Latent Action Diffusion Path) model to incorporate the information diffusion model with the model of external trends, and then design an EM-based algorithm to infer the diffusion probabilities, the external trends and the sources of events efficiently.
REF proposed an LADP model that improves the learning by extracting social events from data streams.
Extracting social events for learning better information diffusion models
KDD '13
Computer Science