title: Graph Regularized Meta-path Based Transductive Regression in Heterogeneous Information Network.
abstract: 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 (Grempt), which combines the principal philosophies of typical graph-based transductive classification methods and transductive regression models designed for homogeneous networks. The computation of our method is time and space efficient and the precision of our model can be verified by numerical experiments.
authors.affiliation: University of Illinois at Urbana-Champaign.
authors.forename: Mengting
authors.initials: M
authors.lastname: Wan
authors.affiliation: University of Illinois at Urbana-Champaign.
authors.forename: Yunbo
authors.initials: Y
authors.lastname: Ouyang
authors.affiliation: U.S. Army Research Laboratory.
authors.forename: Lance
authors.initials: L
authors.lastname: Kaplan
authors.affiliation: University of Illinois at Urbana-Champaign.
authors.forename: Jiawei
authors.initials: J
authors.lastname: Han