  Graph embedding techniques are a staple of modern graph learning research.
When using embeddings for downstream tasks such as classification, information
about their stability and robustness, i.e., their susceptibility to sources of
noise, stochastic effects, or specific parameter choices, becomes increasingly
important. As one of the most prominent graph embedding schemes, we focus on
node2vec and analyse its embedding quality from multiple perspectives. Our
findings indicate that embedding quality is unstable with respect to parameter
choices, and we propose strategies to remedy this in practice.
