  Most existing popular methods for learning graph embedding only consider
fixed-order global structural features and lack structures hierarchical
representation. To address this weakness, we propose a novel graph embedding
algorithm named GraphCSC that realizes classification based on skeleton
information using fixed-order structures learned in anonymous random walks
manner, and component information using different size subgraphs. 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.
