{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/aesthetic-discrimination-of-graph-layouts","title":"Aesthetic Discrimination of Graph Layouts","arxiv_id":"1809.01017","date":"2018-09-04","proceeding":null,"authors":["Moritz Klammler","Tamara Mchedlidze","Alexey Pak"],"abstract":"This paper addresses the following basic question: given two layouts of the\nsame graph, which one is more aesthetically pleasing? We propose a neural\nnetwork-based discriminator model trained on a labeled dataset that decides\nwhich of two layouts has a higher aesthetic quality. The feature vectors used\nas inputs to the model are based on known graph drawing quality metrics,\nclassical statistics, information-theoretical quantities, and two-point\nstatistics inspired by methods of condensed matter physics. The large corpus of\nlayout pairs used for training and testing is constructed using force-directed\ndrawing algorithms and the layouts that naturally stem from the process of\ngraph generation. It is further extended using data augmentation techniques.\nThe mean prediction accuracy of our model is 95.70%, outperforming\ndiscriminators based on stress and on the linear combination of popular quality\nmetrics by a statistically significant margin.","url_abs":"http://arxiv.org/abs/1809.01017v1","url_pdf":"http://arxiv.org/pdf/1809.01017v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"aesthetic-discrimination-of-graph-layouts","repo_url":"https://github.com/5gon12eder/msc-graphstudy","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"graph-generation","task_name":"Graph Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}