Recent advances in spatial transcriptomics enable the exploration of biological processes between cells at an unprecedented resolution.Leveraging spatial information allows us to construct cell-to-cell interaction graphs that describe possible communication between cells.Combining spatial interactions through graph neural networks (GNNs) with cells' gene expressions is a promising avenue for uncovering the underlying mechanisms behind, for example, cell differentiation.However, how to best construct a meaningful graph that captures relevant spatial information remains an open question.Moreover, what GNN architectures perform well on typical prediction tasks, such as cell-type prediction, is unclear.We address these questions by systematically evaluating several graph construction methods with common GNNs on four publicly available spatial transcriptomics datasets.Our results show that the spatial cell-to-cell interaction graphs contain relevant information for predicting cell types.Despite differences in graph topology, the choice of graph construction method affects cell-type prediction performance only minimally.Common GNN models do not perform better than a simpler multi-layer perceptron that does not have access to spatial information.