Graph Classification via Topological and Label Attributes

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 here 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 better or competitive classification accuracy, and is also substantially faster than other graph kernels. It is the most effective method for large unlabeled graphs.