  Similarity measures are used extensively in machine learning and data science
algorithms. The newly proposed graph Relative Hausdorff (RH) distance is a
lightweight yet nuanced similarity measure for quantifying the closeness of two
graphs. In this work we study the effectiveness of RH distance as a tool for
detecting anomalies in time-evolving graph sequences. We apply RH to cyber data
with given red team events, as well to synthetically generated sequences of
graphs with planted attacks. In our experiments, the performance of RH distance
is at times comparable, and sometimes superior, to graph edit distance in
detecting anomalous phenomena. Our results suggest that in appropriate
contexts, RH distance has advantages over more computationally intensive
similarity measures.
