  Graph learning algorithms have attained state-of-the-art performance on many
graph analysis tasks such as node classification, link prediction, and
clustering. It has, however, become hard to track the field's burgeoning
progress. One reason is due to the very small number of datasets used in
practice to benchmark the performance of graph learning algorithms. This
shockingly small sample size (~10) allows for only limited scientific insight
into the problem.
  In this work, we aim to address this deficiency. We propose to generate
synthetic graphs, and study the behaviour of graph learning algorithms in a
controlled scenario. We develop a fully-featured synthetic graph generator that
allows deep inspection of different models. We argue that synthetic graph
generations allows for thorough investigation of algorithms and provides more
insights than overfitting on three citation datasets. In the case study, we
show how our framework provides insight into unsupervised and supervised graph
neural network models.
