{"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/node-centralities-and-classification","title":"Node Centralities and Classification Performance for Characterizing Node Embedding Algorithms","arxiv_id":"1802.06368","date":"2018-02-18","proceeding":null,"authors":["Kento Nozawa","Masanari Kimura","Atsunori Kanemura"],"abstract":"Embedding graph nodes into a vector space can allow the use of machine\nlearning to e.g. predict node classes, but the study of node embedding\nalgorithms is immature compared to the natural language processing field\nbecause of a diverse nature of graphs. We examine the performance of node\nembedding algorithms with respect to graph centrality measures that\ncharacterize diverse graphs, through systematic experiments with four node\nembedding algorithms, four or five graph centralities, and six datasets.\nExperimental results give insights into the properties of node embedding\nalgorithms, which can be a basis for further research on this topic.","url_abs":"http://arxiv.org/abs/1802.06368v1","url_pdf":"http://arxiv.org/pdf/1802.06368v1.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":"node-centralities-and-classification","repo_url":"https://github.com/nzw0301/iclrw2018","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}