Community Detection with Deep Attention Autoencoder Based on Subspace Constraints

Subspace clustering, known for its effectiveness in handling high-dimensional data, has attracted attention. And the autoencoder can discover hidden features within a large dataset, yet it faces challenges in utilizing attribute information for reconstruction and capturing complex spatial structural information. To tackle these issues, we propose a community detection algorithm named Deep Attention Autoencoder Based on Subspace Constraints (DAASC). First, we design an attribute topology fusion strategy to integrate attribute information into the reconstruction of the decoder. Then, we design a subspace autoencoder strategy, using the concept of subspaces to construct the loss function, to capture the spatial structural information of the data. Experiments conducted on both real-world and synthetic networks to compare DAASC with several state-of-the-art community detection algorithms demonstrate its exceptional accuracy and robustness.