Predicting Properties of Nodes via Community-Aware Features

A community structure that is often present in complex networks plays an important role not only in their formation but also shapes dynamics of these networks, affecting properties of their nodes. In this paper, we propose a family of community-aware node features and then investigate their properties. We show that they have high predictive power for classification tasks. We also verify that they contain information that cannot be recovered neither by classical node features nor by node embeddings (both classical as well as structural).