Ranking-based classification of heterogeneous information networks

Ming Ji, Jiawei Han, Marina Danilevsky

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 subnetwork 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.

Original languageEnglish (US)
Title of host publicationProceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11
Pages1298-1306
Number of pages9
DOIs
StatePublished - Sep 16 2011
Event17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11 - San Diego, CA, United States
Duration: Aug 21 2011Aug 24 2011

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Other

Other17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11
CountryUnited States
CitySan Diego, CA
Period8/21/118/24/11

Keywords

  • Classification
  • Heterogeneous information network
  • Ranking

ASJC Scopus subject areas

  • Software
  • Information Systems

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