  This paper shows how information about the network's community structure can
be used to define node features with high predictive power for classification
tasks. To do so, we define a family of community-aware node features and
investigate their properties. Those features are designed to ensure that they
can be efficiently computed even for large graphs. We show that community-aware
node features contain information that cannot be completely recovered by
classical node features or node embeddings (both classical and structural) and
bring value in node classification tasks. This is verified for various
classification tasks on synthetic and real-life networks.
