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Effective graph classification based on topological and label attributes
Author(s) -
Li Geng,
Semerci Murat,
Yener Bülent,
Zaki Mohammed J.
Publication year - 2012
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.11153
Subject(s) - computer science , pattern recognition (psychology) , graph , mathematics , topology (electrical circuits) , artificial intelligence , theoretical computer science , combinatorics
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. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining, 2012