This master dissertation investigates the effectiveness of generalized information measures for learning on graphs (LoG). The variational graph autoencoders framework proposed by Kipf and Welling (2016b) was modified by generalized divergence measures as part of the learning objective to delimit the research scope. Then, the main contributions of this work are: (i) the kappa-divergences - a unified representation for generalized divergence measures; (ii) two novel families of divergences, delta and eta; and (iii) the generalized graph variational autoencoders (GGVA) - a variational graph autoencoders framework based on κ-divergences. The experiments on LoG, using five citation network datasets and a Brazilian power grid network dataset, indicate that GGVA outperforms baseline models in node classification and link prediction, considering time efficiency and average precision. The qualitative analysis of the learned embeddings of GGVA indicates a good enough capacity to distinguish classes.