  Graphs are often used to organize data because of their simple topological
structure, and therefore play a key role in machine learning. And it turns out
that the low-dimensional embedded representation obtained by graph
representation learning are extremely useful in various typical tasks, such as
node classification, content recommendation and link prediction. However, the
existing methods mostly start from the microstructure (i.e., the edges) in the
graph, ignoring the mesoscopic structure (high-order local structure). Here, we
propose wGCN -- a novel framework that utilizes random walk to obtain the
node-specific mesoscopic structures of the graph, and utilizes these mesoscopic
structures to reconstruct the graph And organize the characteristic information
of the nodes. Our method can effectively generate node embeddings for
previously unseen data, which has been proven in a series of experiments
conducted on citation networks and social networks (our method has advantages
over baseline methods). We believe that combining high-order local structural
information can more efficiently explore the potential of the network, which
will greatly improve the learning efficiency of graph neural network and
promote the establishment of new learning models.
