  Graph Convolutional Networks (GCNs) achieve great success in non-Euclidean
structure data processing recently. In existing studies, deeper layers are used
in CCNs to extract deeper features of Euclidean structure data. However, for
non-Euclidean structure data, too deep GCNs will confront with problems like
"neighbor explosion" and "over-smoothing", it also cannot be applied to large
datasets. To address these problems, we propose a model called PathSAGE, which
can learn high-order topological information and improve the model's
performance by expanding the receptive field. The model randomly samples paths
starting from the central node and aggregates them by Transformer encoder.
PathSAGE has only one layer of structure to aggregate nodes which avoid those
problems above. The results of evaluation shows that our model achieves
comparable performance with the state-of-the-art models in inductive learning
tasks.
