  Recently, many systems for graph analysis have been developed to address the
growing needs of both industry and academia to study complex graphs. Insight
into the practical uses of graph analysis will allow future developments of
such systems to optimize for real-world usage, instead of targeting single use
cases or hypothetical workloads. This insight may be derived from surveys on
the applications of graph analysis. However, existing surveys are limited in
the variety of application domains, datasets, and/or graph analysis techniques
they study. In this work we present and apply a systematic method for
identifying practical use cases of graph analysis. We identify commonly used
graph features and analysis methods and use our findings to construct a
taxonomy of graph analysis applications. We conclude that practical use cases
of graph analysis cover a diverse set of graph features and analysis methods.
Furthermore, most applications combine multiple features and methods. Our
findings motivate further development of graph analysis systems to support a
broader set of applications and to facilitate the combination of multiple
analysis methods in an (interactive) workflow.
