  Comparative graph and network analysis play an important role in both systems
biology and pattern recognition, but existing surveys on the topic have
historically ignored or underserved one or the other of these fields. We
present an integrative introduction to the key objectives and methods of graph
and network comparison in each field, with the intent of remaining accessible
to relative novices in order to mitigate the barrier to interdisciplinary idea
crossover.
  To guide our investigation, and to quantitatively justify our assertions
about what the key objectives and methods of each field are, we have
constructed a citation network containing 5,793 vertices from the full
reference lists of over two hundred relevant papers, which we collected by
searching Google Scholar for ten different network comparison-related search
terms. We investigate its basic statistics and community structure, and frame
our presentation around the papers found to have high importance according to
five different standard centrality measures.
