This document proposes using random features to strengthen graph neural networks (GNNs) for node classification tasks. It summarizes that GNNs cannot distinguish nodes with identical features and are not universal approximators. By adding random features to each node, GNNs can distinguish nodes and tree views, allowing them to detect graph structures like triangles. Experiments on synthetic and re