Effective graph classification based on topological and label attributes

Geng Li, Murat Semerci, Bülent Yener, Mohammed J. Zaki

Research output: Contribution to journalArticle

49 Citations (Scopus)


Graph classification is an important data mining task, and various graph kernel methods have been proposed recently for this task. These methods have proven to be effective, but they tend to have high computational overhead. In this paper, we propose an alternative approach to graph classification that is based on feature vectors constructed from different global topological attributes, as well as global label features. The main idea is that the graphs from the same class should have similar topological and label attributes. Our method is simple and easy to implement, and via a detailed comparison on real benchmark datasets, we show that our topological and label feature-based approach delivers competitive classification accuracy, with significantly better results on those datasets that have large unlabeled graph instances. Our method is also substantially faster than most other graph kernels.

Original languageEnglish
Pages (from-to)265-283
Number of pages19
JournalStatistical Analysis and Data Mining
Issue number4
Publication statusPublished - 1 Aug 2012
Externally publishedYes



  • Graph classification
  • Graph kernels
  • Graph mining
  • Topological attributes

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Analysis

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