  Collective classification has been intensively studied due to its impact in
many important applications, such as web mining, bioinformatics and citation
analysis. Collective classification approaches exploit the dependencies of a
group of linked objects whose class labels are correlated and need to be
predicted simultaneously. In this paper, we focus on studying the collective
classification problem in heterogeneous networks, which involves multiple types
of data objects interconnected by multiple types of links. Intuitively, two
objects are correlated if they are linked by many paths in the network.
However, most existing approaches measure the dependencies among objects
through directly links or indirect links without considering the different
semantic meanings behind different paths. In this paper, we study the
collective classification problem taht is defined among the same type of
objects in heterogenous networks. Moreover, by considering different linkage
paths in the network, one can capture the subtlety of different types of
dependencies among objects. We introduce the concept of meta-path based
dependencies among objects, where a meta path is a path consisting a certain
sequence of linke types. We show that the quality of collective classification
results strongly depends upon the meta paths used. To accommodate the large
network size, a novel solution, called HCC (meta-path based Heterogenous
Collective Classification), is developed to effectively assign labels to a
group of instances that are interconnected through different meta-paths. The
proposed HCC model can capture different types of dependencies among objects
with respect to different meta paths. Empirical studies on real-world networks
demonstrate that effectiveness of the proposed meta path-based collective
classification approach.
