Detecting overlapping communities is a fundamental challenge in network analysis. Communities represent groups of nodes that are more densely connected internally than to the rest of the network, often corresponding to functional modules or social groups in real-world systems. This paper proposes a hybrid framework called GAE.SC that combines a Graph Autoencoder (GAE) with a semi-supervised fuzzy clustering algorithm to address this task. Our model first employs a GAE, trained directly on the input network, to learn low-dimensional, structure-preserving node embeddings from both graph topology and node attributes. These embeddings subsequently serve as input for a semi-supervised clustering algorithm, which incorporates supervision constraints to enhance the accuracy and interpretability of the community detection process. Experimental results on five benchmark datasets demonstrate that our method consistently outperforms GCNFCM, a validated and robust baseline for overlapping community detection in complex networks.