Discriminative frequent subgraph mining with optimality guarantees

• Published in: Statistical analysis and data mining Vol. 3; no. 5; pp. 302 - 318
• Main Authors: Thoma, Marisa, Cheng, Hong, Gretton, Arthur, Han, Jiawei, Kriegel, Hans-Peter, Smola, Alex, Song, Le, Yu, Philip S., Yan, Xifeng, Borgwardt, Karsten M.
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.10.2010
• Subjects: feature selection; frequent subgraphs; graph mining; nested selection; submodularity
• ISSN: 1932-1864; 1932-1872
• DOI: 10.1002/sam.10084

The goal of frequent subgraph mining is to detect subgraphs that frequently occur in a dataset of graphs. In classification settings, one is often interested in discovering discriminative frequent subgraphs, whose presence or absence is indicative of the class membership of a graph. In this article, we propose an approach to feature selection on frequent subgraphs, called CORK, that combines two central advantages. First, it optimizes a submodular quality criterion, which means that we can yield a near‐optimal solution using greedy feature selection. Second, our submodular quality function criterion can be integrated into gSpan, the state‐of‐the‐art tool for frequent subgraph mining, and help to prune the search space for discriminative frequent subgraphs even during frequent subgraph mining. Copyright © 2010 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 3: 302‐318, 2010