  Data analysts commonly utilize statistics to summarize large datasets. While
it is often sufficient to explore only the summary statistics of a dataset
(e.g., min/mean/max), Anscombe's Quartet demonstrates how such statistics can
be misleading. We consider a similar problem in the context of graph mining. To
study the relationships between different graph properties, we examine
low-order non-isomorphic graphs and provide a simple visual analytics system to
explore correlations across multiple graph properties. However, for larger
graphs, studying the entire space quickly becomes intractable. We use different
random graph generation methods to further look into the distribution of graph
properties for higher order graphs and investigate the impact of various
sampling methodologies. We also describe a method for generating many graphs
that are identical over a number of graph properties and statistics yet are
clearly different and identifiably distinct.

Computational Geometry