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Graph embedding techniques have been increasingly deployed in a multitude ofdifferent applications that involve learning on non-Euclidean data. However,existing graph embedding models either fail to incorporate node attributeinformation during training or suffer from node attribute noise, whichcompromises the accuracy. Moreover, very few of them scale to large graphs dueto their high computational complexity and memory usage. In this paper wepropose GraphZoom, a multi-level framework for improving both accuracy andscalability of unsupervised graph embedding algorithms. GraphZoom firstperforms graph fusion to generate a new graph that effectively encodes thetopology of the original graph and the node attribute information. This fusedgraph is then repeatedly coarsened into much smaller graphs by merging nodeswith high spectral similarities. GraphZoom allows any existing embeddingmethods to be applied to the coarsened graph, before it progressively refinethe embeddings obtained at the coarsest level to increasingly finer graphs. Wehave evaluated our approach on a number of popular graph datasets for bothtransductive and inductive tasks. Our experiments show that GraphZoom cansubstantially increase the classification accuracy and significantly acceleratethe entire graph embedding process by up to 40.8x, when compared to thestate-of-the-art unsupervised embedding methods.

GraphZoom: A multi-level spectral approach for accurate and scalable graph embedding