document: Ranking-based classification of heterogeneous information networks It has been recently recognized that heterogeneous information networks composed of multiple types of nodes and links are prevalent in the real world . Both classification and ranking of the nodes ( or data objects ) in such networks are essential for network analysis . However , so far these approaches have generally been performed separately . In this paper , we combine ranking and classification in order to perform more accurate analysis of a heterogeneous information network . Our intuition is that highly ranked objects within a class should play more important roles in classification . On the other hand , class membership information is important for determining a quality ranking over a dataset . We believe it is therefore beneficial to integrate classification and ranking in a simultaneous , mutually enhancing process , and to this end , propose a novel ranking-based iterative classification framework , called RankClass . Specifically , we build a graph-based ranking model to iteratively compute the ranking distribution of the objects within each class . At each iteration , according to the current ranking results , the graph structure used in the ranking algorithm is adjusted so that the sub-network corresponding to the specific class is emphasized , while the rest of the network is weakened . As our experiments show , integrating ranking with classification not only generates more accurate classes than the state-of-art classification methods on networked data , but also provides meaningful ranking of objects within each class , serving as a more informative view of the data than traditional classification .
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