A Cluster Based Ranking Framework for Multi-typed Information Networks

A multi-typed information network is an information network which contains multiple types of objects having actions and interactions between each other. Although many studies on single typed information network haven been found in the literature, only a little has been known concerning with multi-typed information networks. On the other hand, multiple type information networks are ubiquitous and forming an important component of modern information infrastructure. Thus, in this paper we propose a new method to give a better understanding of information networks and their properties. Specifically we propose a new cluster based ranking system for multi-typed information networks. In this aspect, ranking evaluates objects of information networks based on some mathematical ranking function which illustrates the characteristic of objects with which any two objects of the same type can be compared by qualitatively. Moreover, clustering group objects is based on a certain measure such that similar objects are in the same cluster whereas dissimilar objects are in different clusters. Then the ranking and clustering processes are integrated to extract insight overall views of information networks, so that the integrated method can be widely applied in different information network settings. Our experiments using DBLP datasets can generate good informative clusters producing reliable ranking system.