Fan, YumingHong, Seok-HeeMeidiana, AmyraEl-Assady, MennatallahOttley, AlvittaTominski, Christian2025-05-262025-05-262025978-3-03868-282-0https://doi.org/10.2312/evs.20251090https://diglib.eg.org/handle/10.2312/evs20251090Neighborhood faithfulness metrics measure how faithfully the ground truth neighbors of vertices in a graph G are represented as the geometric neighbors of vertices in a drawing D of G. In this paper, we present NFGD, a post-processing algorithm for optimizing the neighborhood faithfulness of graph drawings. Experiments demonstrate the effectiveness of NFGD for computing neighbor-faithful drawings, on average 320% improvement over the popular graph drawing algorithms: 425% over Stress Majorization (SM) and 215% over force-directed algorithm Fruchterman-Reingold (FR). In particular, for scale-free graphs, NFGD-SM achieves 776% improvement over SM and NFGD-FR obtains 597% improvement over FR.Attribution 4.0 International LicenseCCS Concepts: Human-centered computing → Graph drawingsHuman centered computing → Graph drawings10.2312/evs.202510905 pages