  Several natural phenomena and complex systems are often represented as
networks. Discovering their community structure is a fundamental task for
understanding these networks. Many algorithms have been proposed, but recently,
Graph Neural Networks (GNN) have emerged as a compelling approach for enhancing
this task.In this paper, we introduce a simple, efficient, and
clustering-oriented model based on unsupervised \textbf{G}raph Attention
\textbf{A}uto\textbf{E}ncoder for community detection in attributed networks
(GAECO). The proposed model adeptly learns representations from both the
network's topology and attribute information, simultaneously addressing dual
objectives: reconstruction and community discovery. It places a particular
emphasis on discovering compact communities by robustly minimizing clustering
errors. The model employs k-means as an objective function and utilizes a
multi-head Graph Attention Auto-Encoder for decoding the representations.
Experiments conducted on three datasets of attributed networks show that our
method surpasses state-of-the-art algorithms in terms of NMI and ARI.
Additionally, our approach scales effectively with the size of the network,
making it suitable for large-scale applications. The implications of our
findings extend beyond biological network interpretation and social network
analysis, where knowledge of the fundamental community structure is essential.
