NFGD: Neighborhood-Faithful Graph Drawing
| dc.contributor.author | Fan, Yuming | en_US |
| dc.contributor.author | Hong, Seok-Hee | en_US |
| dc.contributor.author | Meidiana, Amyra | en_US |
| dc.contributor.editor | El-Assady, Mennatallah | en_US |
| dc.contributor.editor | Ottley, Alvitta | en_US |
| dc.contributor.editor | Tominski, Christian | en_US |
| dc.date.accessioned | 2025-05-26T06:59:05Z | |
| dc.date.available | 2025-05-26T06:59:05Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | Neighborhood 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. | en_US |
| dc.description.sectionheaders | Techniques and Tools | |
| dc.description.seriesinformation | EuroVis 2025 - Short Papers | |
| dc.identifier.doi | 10.2312/evs.20251090 | |
| dc.identifier.isbn | 978-3-03868-282-0 | |
| dc.identifier.pages | 5 pages | |
| dc.identifier.uri | https://doi.org/10.2312/evs.20251090 | |
| dc.identifier.uri | https://diglib.eg.org/handle/10.2312/evs20251090 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.rights | Attribution 4.0 International License | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | CCS Concepts: Human-centered computing → Graph drawings | |
| dc.subject | Human centered computing → Graph drawings | |
| dc.title | NFGD: Neighborhood-Faithful Graph Drawing | en_US |
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