ABSTRACT
In Chapter 3, we proposed a general model, the relation summary network, to find the hidden structures (the local cluster structures and the global community structures) from a k-partite heterogeneous relation graph. The model provides a principal framework for unsupervised learning on k-partite heterogeneous relation graphs of various structures. In this chapter, we derive a novel algorithm to identify the hidden structures of a k-partite heterogeneous relation graph by constructing a relation summary network to approximate the original k-partite heterogeneous relation graph under a broad range of distortion measures. We also establish the connections between existing clustering approaches and the proposed model to provide a unified view to the clustering approaches.
