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Netw. Anal. Min."],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>This paper shows how information about the network\u2019s 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. 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