  The graph structure is a commonly used data storage mode, and it turns out
that the low-dimensional embedded representation of nodes in the graph is
extremely useful in various typical tasks, such as node classification, link
prediction , etc. However, most of the existing approaches start from the
binary relationship (i.e., edges) in the graph and have not leveraged the
higher order local structure (i.e., motifs) of the graph. Here, we propose
mGCMN -- a novel framework which utilizes node feature information and the
higher order local structure of the graph to effectively generate node
embeddings for previously unseen data. Through research we have found that
different types of networks have different key motifs. And the advantages of
our method over the baseline methods have been demonstrated in a large number
of experiments on citation network and social network datasets. At the same
time, a positive correlation between increase of the classification accuracy
and the clustering coefficient is revealed. It is believed that using high
order structural information can truly manifest the potential of the network,
which will greatly improve the learning efficiency of the graph neural network
and promote a brand-new learning mode establishment.
