{"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/graphtsne-a-visualization-technique-for-graph","title":"GraphTSNE: A Visualization Technique for Graph-Structured Data","arxiv_id":"1904.06915","date":"2019-04-15","proceeding":null,"authors":["Yao Yang Leow","Thomas Laurent","Xavier Bresson"],"abstract":"We present GraphTSNE, a novel visualization technique for graph-structured\ndata based on t-SNE. The growing interest in graph-structured data increases\nthe importance of gaining human insight into such datasets by means of\nvisualization. Among the most popular visualization techniques, classical t-SNE\nis not suitable on such datasets because it has no mechanism to make use of\ninformation from the graph structure. On the other hand, visualization\ntechniques which operate on graphs, such as Laplacian Eigenmaps and tsNET, have\nno mechanism to make use of information from node features. Our proposed method\nGraphTSNE produces visualizations which account for both graph structure and\nnode features. It is based on scalable and unsupervised training of a graph\nconvolutional network on a modified t-SNE loss. By assembling a suite of\nevaluation metrics, we demonstrate that our method produces desirable\nvisualizations on three benchmark datasets.","url_abs":"http://arxiv.org/abs/1904.06915v3","url_pdf":"http://arxiv.org/pdf/1904.06915v3.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":"graphtsne-a-visualization-technique-for-graph","repo_url":"https://github.com/leowyy/GraphTSNE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}