Graph neural networks (GNNs) have become the de facto approach for supervised learning on graph data.To train these networks, most practitioners employ the categorical cross-entropy (CE) loss. We can attribute this largely to the probabilistic interpretability of models trained using CE, since it corresponds to the negative log of the categorical/softmax likelihood.We can attribute this largely to the probabilistic interpretation of CE, since it corresponds to the negative log of the categorical/softmax likelihood.Nonetheless, recent works have shown that deep learning models can benefit from adopting other loss functions. For instance, neural networks trained with symmetric losses (e.g., mean absolute error) are robust to label noise. Nonetheless, loss functions are a modeling choice and other training criteria can be employed — e.g., hinge loss and mean absolute error (MAE). Perhaps surprisingly, the effect of using different losses on GNNs has not been explored. In this preliminary work, we gauge the impact of different loss functions to the performance of GNNs for node classification under i) noisy labels and ii) different sample sizes. In contrast to findings on Euclidean domains, our results for GNNs show that there is no significant difference between models trained with CE and other classical loss functions on both aforementioned scenarios.

Variational auto-encoding architectures represent one of the most popular approaches to graph generative modeling. These models comprise encoder and a decoder networks, which map back and forth between the input and latent spaces. Notably, most of the literature in variational autoencoders (VAEs) for graphs focuses on developing more efficient architectures at the expense of increased complexity. In this work, we pursue an orthogonal direction and leverage multi-hop linear graph convolutional layers to create efficient yet simple encoders, boosting the performance of graph autoencoders. Our results demonstrate that our approach outperforms popular graph VAE baselines in link prediction tasks.