We propose a model for joint representation learning and clustering of attributed graphs. Based on the simple graph convolutional network, our model performs clustering by minimizing the difference between the low representaion space of the convolved data and the reconstruction of the centroids in the embedding space. The experiments show the effectiveness of the derived model against state-of-the-art methods on different attributed graph datasets for both clustering and visualization purposes (Fettal et al., 2022).