  The defect morphology is an essential aspect of the evolution of crystals'
microstructure and its response to stress. Existing methods either only report
defect concentration or characterize only some of the defect morphologies. The
need for an efficient and comprehensive algorithm to study defects is becoming
more evident with the increase in the amount of simulation data and
improvements in data-driven algorithms.
  We present a method to characterize a defect's morphology precisely by
reducing the problem into graph theoretical concepts of finding connected
components and cycles. The algorithm can identify the different homogenous
components within a defect cluster having mixed morphology. We apply the method
to classify morphologies of over a thousand point defect clusters formed in
high energy W collision cascades. We highlight our method's comparative
advantage for its completeness, computational speed, and quantitative details.
