  Network alignment is useful for multiple applications that require
increasingly large graphs to be processed. Existing research approaches this as
an optimization problem or computes the similarity based on node
representations. However, the process of aligning every pair of nodes between
relatively large networks is time-consuming and resource-intensive. In this
paper, we propose a framework, called G-CREWE (Graph CompREssion With
Embedding) to solve the network alignment problem. G-CREWE uses node embeddings
to align the networks on two levels of resolution, a fine resolution given by
the original network and a coarse resolution given by a compressed version, to
achieve an efficient and effective network alignment. The framework first
extracts node features and learns the node embedding via a Graph Convolutional
Network (GCN). Then, node embedding helps to guide the process of graph
compression and finally improve the alignment performance. As part of G-CREWE,
we also propose a new compression mechanism called MERGE (Minimum dEgRee
neiGhbors comprEssion) to reduce the size of the input networks while
preserving the consistency in their topological structure. Experiments on all
real networks show that our method is more than twice as fast as the most
competitive existing methods while maintaining high accuracy.
