  The metric dimension of a graph is the smallest number of nodes required to
identify all other nodes based on shortest path distances uniquely.
Applications of metric dimension include discovering the source of a spread in
a network, canonically labeling graphs, and embedding symbolic data in
low-dimensional Euclidean spaces. This survey gives a self-contained
introduction to metric dimension and an overview of the quintessential results
and applications. We discuss methods for approximating the metric dimension of
general graphs, and specific bounds and asymptotic behavior for deterministic
and random families of graphs. We conclude with related concepts and directions
for future work.
