Discovering time-evolving influence from dynamic heterogeneous graphs

Influence among objects prevalently exists in graph structured data. However, most existing research efforts detect influence among objects from snapshots of homogeneous graphs. In this paper, we study a new problem of detecting time-evolving influence among objects from dynamic heterogeneous graphs. We propose a probabilistic graphical model, Time-evolving Influence Model (TIM), to capture the temporal dynamics of graphs, in which the time-evolving influence is hidden, and to leverage the information from heterogeneous graphs, with which we can improve the learned knowledge. To learn the graphical model, we design both non-parallel and parallel Gibbs sampling algorithms. We conduct extensive experiments on both synthetic and real data sets to show the effectiveness of the proposed model and the efficiency of the learning algorithms.