  Graph models, like other machine learning models, have implicit and explicit
biases built-in, which often impact performance in nontrivial ways. The model's
faithfulness is often measured by comparing the newly generated graph against
the source graph using any number or combination of graph properties.
Differences in the size or topology of the generated graph, therefore, indicate
a loss in the model. Yet, in many systems, errors encoded in loss functions are
subtle and not well understood. In the present work, we introduce the Infinity
Mirror test for analyzing the robustness of graph models. This straightforward
stress test works by repeatedly fitting a model to its own outputs. A
hypothetically perfect graph model would have no deviation from the source
graph; however, the model's implicit biases and assumptions are exaggerated by
the Infinity Mirror test, exposing potential issues that were previously
obscured. Through an analysis of thousands of experiments on synthetic and
real-world graphs, we show that several conventional graph models degenerate in
exciting and informative ways. We believe that the observed degenerative
patterns are clues to the future development of better graph models.
