Selecting Information Diffusion Models over Social Networks for Behavioral Analysis

We investigate how well different information diffusion models can explain observation data by learning their parameters and discuss which model is better suited to which topic. We use two models (AsIC, AsLT), each of which is an extension of the well known Independent Cascade (IC) and Linear Threshold (LT) models and incorporates asynchronous time delay. The model parameters are learned by maximizing the likelihood of observation, and the model selection is performed by choosing the one with better predictive accuracy. We first show by using four real networks that the proposed learning algorithm correctly learns the model parameters both accurately and stably, and the proposed selection method identifies the correct diffusion model from which the data are generated. We next apply these methods to behavioral analysis of topic propagation using the real blog propagation data, and show that although the relative propagation speed of topics that are derived from the learned parameter values is rather insensitive to the model selected, there is a clear indication as to which topic better follows which model. The correspondence between the topic and the model selected is well interpretable.