Change point detection for information diffusion tree

We propose a method of detecting the points at which the speed of information diffusion changed from an observed diffusion sequence data over a social network, explicitly taking the network structure into account. Thus, change in diffusion is both spatial and temporal. This is different from most of the existing change detection approaches in which all the diffusion information is projected on a single time line and the search is made in this time axis. We formulate this as a search problem of change points and their respective change rates under the framework of maximum log-likelihood embedded in MDL. Time complexity of the search is almost proportional to the number of observed data points and the method is very efficient. We tested this using both a real Twitter date (ground truth not known) and the synthetic data (ground truth known), and demonstrated that the proposed method can detect the change points efficiently and the results are very different from the existing sequence-based (time axis) approach (Kleinberg’s method).