Format

Send to

Choose Destination
Proc SIAM Int Conf Data Min. 2015 Apr-May;2015:918-926.

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

Author information

1
University of Illinois at Urbana-Champaign.
2
U.S. Army Research Laboratory.

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.

Supplemental Content

Full text links

Icon for PubMed Central
Loading ...
Support Center