  Graph embedding techniques have been increasingly deployed in a multitude of
different applications that involve learning on non-Euclidean data. However,
existing graph embedding models either fail to incorporate node attribute
information during training or suffer from node attribute noise, which
compromises the accuracy. Moreover, very few of them scale to large graphs due
to their high computational complexity and memory usage. In this paper we
propose GraphZoom, a multi-level framework for improving both accuracy and
scalability of unsupervised graph embedding algorithms. GraphZoom first
performs graph fusion to generate a new graph that effectively encodes the
topology of the original graph and the node attribute information. This fused
graph is then repeatedly coarsened into much smaller graphs by merging nodes
with high spectral similarities. GraphZoom allows any existing embedding
methods to be applied to the coarsened graph, before it progressively refine
the embeddings obtained at the coarsest level to increasingly finer graphs. We
have evaluated our approach on a number of popular graph datasets for both
transductive and inductive tasks. Our experiments show that GraphZoom can
substantially increase the classification accuracy and significantly accelerate
the entire graph embedding process by up to 40.8x, when compared to the
state-of-the-art unsupervised embedding methods.
