  While advances in computing resources have made processing enormous amounts
of data possible, human ability to identify patterns in such data has not
scaled accordingly. Efficient computational methods for condensing and
simplifying data are thus becoming vital for extracting actionable insights. In
particular, while data summarization techniques have been studied extensively,
only recently has summarizing interconnected data, or graphs, become popular.
This survey is a structured, comprehensive overview of the state-of-the-art
methods for summarizing graph data. We first broach the motivation behind, and
the challenges of, graph summarization. We then categorize summarization
approaches by the type of graphs taken as input and further organize each
category by core methodology. Finally, we discuss applications of summarization
on real-world graphs and conclude by describing some open problems in the
field.
