Abstract 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 is that the graphs from the same class should have s.
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