2011 RankingbasedClassificationofHet

(Ji et al., 2011) ⇒ Ming Ji, Jiawei Han, and Marina Danilevsky. (2011). “Ranking-based Classification of Heterogeneous Information Networks.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011) Journal. ISBN:978-1-4503-0813-7 doi:10.1145/2020408.2020603

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Author Keywords

Algorithms; classification; data mining; heterogeneous information network; ranking

Abstract

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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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 RankingbasedClassificationofHet	Ming Ji Marina Danilevsky Jiawei Han			Ranking-based Classification of Heterogeneous Information Networks				10.1145/2020408.2020603		2011