Information Spread and Topic Diffusion in Heterogeneous Information Networks.

Diffusion of information in complex networks largely depends on the network structure. Recent studies have mainly addressed information diffusion in homogeneous networks where there is only a single type of nodes and edges. However, some real-world networks consist of heterogeneous types of nodes and edges. In this manuscript, we model information diffusion in heterogeneous information networks, and use interactions of different meta-paths to predict the diffusion process. A meta-path is a path between nodes across different layers of a heterogeneous network. As its most important feature the proposed method is capable of determining the influence of all meta-paths on the diffusion process. A conditional probability is used assuming interdependent relations between the nodes to calculate the activation probability of each node. As independent cascade models, we consider linear threshold and independent cascade models. Applying the proposed method on two real heterogeneous networks reveals its effectiveness and superior performance over state-of-the-art methods.