Graph classification based on pattern co-occurrence

Ning Jin, Calvin Young, Wei Wang
2009 Proceeding of the 18th ACM conference on Information and knowledge management - CIKM '09  
Subgraph patterns are widely used in graph classification, but their effectiveness is often hampered by large number of patterns or lack of discrimination power among individual patterns. We introduce a novel classification method based on pattern cooccurrence to derive graph classification rules. Our method employs a pattern exploration order such that the complementary discriminative patterns are examined first. Patterns are grouped into co-occurrence rules during the pattern exploration,
more » ... ing to an integrated process of pattern mining and classifier learning. By taking advantage of co-occurrence information, our method can generate strong features by assembling weak features. Unlike previous methods that invoke the pattern mining process repeatedly, our method only performs pattern mining once. In addition, our method produces a more interpretable classifier and shows better or competitive classification effectiveness in terms of accuracy and execution time.
doi:10.1145/1645953.1646027 dblp:conf/cikm/JinYW09 fatcat:qhnrgqmi4fakrlcc4fsclzlw7e