Empirical comparison of graph classification algorithms

The graph classification problem is learning to classify separate, individual graphs in a graph database into two or more categories. A number of algorithms have been introduced for the graph classification problem. We present an empirical comparison of the major approaches for graph classification introduced in literature, namely, SubdueCL, frequent subgraph mining in conjunction with SVMs, walkbased graph kernel, frequent subgraph mining in conjunction with AdaBoost and DT-CLGBI. Experiments are performed on five real world data sets from the Mutagenesis and Predictive Toxicology domain which are considered benchmark data sets for the graph classification problem. Additionally, experiments are performed on a corpus of artificial data sets constructed to investigate the performance of the algorithms across a variety of parameters of interest. Our conclusions are as follows. In datasets where the underlying concept has a high average degree, walk-based graph kernels perform poorly as c...

• Type: Conference
• Year: 2009
• Where: CIDM
• Authors: Nikhil S. Ketkar, Lawrence B. Holder, Diane J. Cook
• Keywords: Artificial Intelligence, CIDM 2009, Frequent Subgraph Mining, Graph Classification Problem, Graph Kernels