Predicting Properties of Nodes via Community-Aware Features
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 21 of its 58 references that are in the map. First posted on arXiv 8 Nov 2023.
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21 of 58 references.
References · 21
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9 more
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