Graph classification based on pattern co-occurrence

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, leading 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. Categories and Subje...

• Type: Conference
• Year: 2009
• Where: CIKM
• Authors: Ning Jin, Calvin Young, Wei Wang
• Keywords: CIKM 2009, Database, Graph Classification, Pattern Exploration, Pattern Mining