  Graph embedding is a transformation of nodes of a network into a set of
vectors. A good embedding should capture the underlying graph topology and
structure, node-to-node relationship, and other relevant information about the
graph, its subgraphs, and nodes themselves. If these objectives are achieved,
an embedding is a meaningful, understandable, and often compressed
representation of a network. Unfortunately, selecting the best embedding is a
challenging task and very often requires domain experts. In this paper, we
extend the framework for evaluating graph embeddings that was recently
introduced by the authors. Now, the framework assigns two scores, local and
global, to each embedding that measure the quality of an evaluated embedding
for tasks that require good representation of local and, respectively, global
properties of the network. The best embedding, if needed, can be selected in an
unsupervised way, or the framework can identify a few embeddings that are worth
further investigation. The framework is flexible, scalable, and can deal with
undirected/directed, weighted/unweighted graphs.
