  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. Graph mining has a similar problem in that graph statistics
(e.g., density, connectivity, clustering coefficient) may not capture all of
the critical properties of a given graph. To study the relationships between
different graph properties and statistics, we examine all low-order (<= 10)
non-isomorphic graphs and provide a simple visual analytics system to explore
correlations across multiple graph properties. However, for graphs with more
than ten nodes, generating the entire space of graphs becomes quickly
intractable. We use different random graph generation methods to further look
into the distribution of graph statistics 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.
