  Graph Nerual Networks (GNNs) are effective models in graph embedding. It
extracts shallow features and neighborhood information by aggregating neighbor
information to learn the embedding representation of different nodes. However,
the local topology information of many nodes in the network is similar, the
network obtained by shallow embedding represents the network that is
susceptible to structural noise, and the low-order embedding cannot capture the
high-order network structure; on the other hand, the deep embedding undergoes
multi-layer convolution. After the filters are stacked, the embedded
distribution is destroyed, and graph smoothing occurs. To address these
challenges, we propose a new framework that leverages network motifs to learn
deep features of the network from low-level embeddings under the assumption of
network homogeneity and transitivity, and then combines local neighborhood
information with deeper global information fusion results in accurate
representation of nodes. On real datasets, our model achieves significant
performance improvement in link prediction and node classification.
