Improving Graph Classification by Means of Linear Combinations of Reduced Graphs

The development and research of graph-based matching techniques that are both computationally efficient and accurate is a pivotal task due to the rapid growth of data acquisition and the omnipresence of structural data. In the present paper, we propose a novel framework using information gained from diversely reduced graph spaces to improve the classification accuracy of a structural classifier. The basic idea consists of three subsequent steps. First, the original graphs are reduced to different size levels with the aid of node centrality measures. Second, we compute the distances between the reduced graphs in the corresponding graph subspaces. Finally, the distances are linearly combined and fed into a distance-based classifier to produce the final classification. On six graph datasets we empirically demonstrate that classifiers clearly benefit from the combined distances obtained in the graph subspaces.