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Constructing latent vector representation for nodes in a network throughembedding models has shown its practicality in many graph analysisapplications, such as node classification, clustering, and link prediction.However, despite the high efficiency and accuracy of learning an embeddingmodel, people have little clue of what information about the original networkis preserved in the embedding vectors. The abstractness of low-dimensionalvector representation, stochastic nature of the construction process, andnon-transparent hyper-parameters all obscure understanding of network embeddingresults. Visualization techniques have been introduced to facilitate embeddingvector inspection, usually by projecting the embedding space to atwo-dimensional display. Although the existing visualization methods allowsimple examination of the structure of embedding space, they cannot supportin-depth exploration of the embedding vectors. In this paper, we design anexploratory visual analytics system that supports the comparative visualinterpretation of embedding vectors at the cluster, instance, and structurallevels. To be more specific, it facilitates comparison of what and how nodemetrics are preserved across different embedding models and investigation ofrelationships between node metrics and selected embedding vectors. Several casestudies confirm the efficacy of our system. Experts' feedback suggests that ourapproach indeed helps them better embrace the understanding of networkembedding models.

EmbeddingVis: A Visual Analytics Approach to Comparative Network Embedding Inspection