  Constructing latent vector representation for nodes in a network through
embedding models has shown its practicality in many graph analysis
applications, such as node classification, clustering, and link prediction.
However, despite the high efficiency and accuracy of learning an embedding
model, people have little clue of what information about the original network
is preserved in the embedding vectors. The abstractness of low-dimensional
vector representation, stochastic nature of the construction process, and
non-transparent hyper-parameters all obscure understanding of network embedding
results. Visualization techniques have been introduced to facilitate embedding
vector inspection, usually by projecting the embedding space to a
two-dimensional display. Although the existing visualization methods allow
simple examination of the structure of embedding space, they cannot support
in-depth exploration of the embedding vectors. In this paper, we design an
exploratory visual analytics system that supports the comparative visual
interpretation of embedding vectors at the cluster, instance, and structural
levels. To be more specific, it facilitates comparison of what and how node
metrics are preserved across different embedding models and investigation of
relationships between node metrics and selected embedding vectors. Several case
studies confirm the efficacy of our system. Experts' feedback suggests that our
approach indeed helps them better embrace the understanding of network
embedding models.
