We demonstrate that a graph-based search algorithm—relying on the construction of an approximate neighborhood graph—can directly work with challenging non-metric and/or non-symmetric distances without resorting to metric-space mapping and/or distance symmetrization, which, in turn, lead to substantial performance degradation. Although the straightforward metrization and symmetrization is usually ineffective, we find that constructing an index using a modified, e.g., symmetrized, distance can improve performance. This observation paves a way to a new line of research of designing index-specific graph-construction distance functions.
Boytsov, L., & Nyberg, E. (2019). Accurate and Fast Retrieval for Complex Non-metric Data via Neighborhood Graphs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11807 LNCS, pp. 128–142). Springer. https://doi.org/10.1007/978-3-030-32047-8_12