Relative Hausdorff Distance for Network Analysis
This work addresses anomaly detection in network analysis, offering a computationally efficient alternative for specific contexts, though it appears incremental as it builds on existing similarity measures.
The paper tackled the problem of detecting anomalies in time-evolving graph sequences by proposing the Relative Hausdorff (RH) distance as a lightweight similarity measure, finding that its performance is comparable or superior to graph edit distance in experiments on cyber data and synthetic graphs.
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.