Graph classification based on skeleton and component features

Most existing popular methods for learning graph embedding only consider fixed-order global structural features but lack hierarchical representation for structures. To address this weakness, we propose a novel graph embedding algorithm named GraphCSC that realizes classification leveraging skeleton information from anonymous random walks with fixed-order length, and component information derived from subgraphs with different sizes. Two graphs are similar if their skeletons and components are both similar. Thus in our model, we integrate both of them together into embeddings as graph homogeneity characterization. We demonstrate our model on different datasets in comparison with a comprehensive list of up-to-date state-of-the-art baselines, and experiments show that our work is superior in real-world graph classification tasks.