Data Clustering and Representation Learning Based on Networked Data

Abstract To deal simultaneously with both, the attributed network embedding and clustering, we propose a new model exploiting both content and structure information. The proposed model relies on the approximation of the relaxed continuous embedding solution by the true discrete clustering. Thereby, we show that incorporating an embedding representation provides simpler and easier interpretable solutions. Experiment results demonstrate that the proposed algorithm performs better, in terms of clustering, than the state-of-art algorithms, including deep learning methods devoted to similar tasks.