Graph Regularized Meta-path Based Transductive Regression in Heterogeneous Information Network.

A number of real-world networks are heterogeneous information networks, which are composed of different types of nodes and links. Numerical prediction in heterogeneous information networks is a challenging but significant area because network based information for unlabeled objects is usually limited to make precise estimations. In this paper, we consider a graph regularized meta-path based transductive regression model (), which combines the principal philosophies of typical graph-based transductive classification methods and transductive regression models designed for homogeneous networks.