{"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/name-disambiguation-in-anonymized-graphs","title":"Name Disambiguation in Anonymized Graphs using Network Embedding","arxiv_id":"1702.02287","date":"2017-02-08","proceeding":null,"authors":["Baichuan Zhang","Mohammad Al Hasan"],"abstract":"In real-world, our DNA is unique but many people share names. This phenomenon\noften causes erroneous aggregation of documents of multiple persons who are\nnamesake of one another. Such mistakes deteriorate the performance of document\nretrieval, web search, and more seriously, cause improper attribution of credit\nor blame in digital forensic. To resolve this issue, the name disambiguation\ntask is designed which aims to partition the documents associated with a name\nreference such that each partition contains documents pertaining to a unique\nreal-life person. Existing solutions to this task substantially rely on feature\nengineering, such as biographical feature extraction, or construction of\nauxiliary features from Wikipedia. However, for many scenarios, such features\nmay be costly to obtain or unavailable due to the risk of privacy violation. In\nthis work, we propose a novel name disambiguation method. Our proposed method\nis non-intrusive of privacy because instead of using attributes pertaining to a\nreal-life person, our method leverages only relational data in the form of\nanonymized graphs. In the methodological aspect, the proposed method uses a\nnovel representation learning model to embed each document in a low dimensional\nvector space where name disambiguation can be solved by a hierarchical\nagglomerative clustering algorithm. Our experimental results demonstrate that\nthe proposed method is significantly better than the existing name\ndisambiguation methods working in a similar setting.","url_abs":"http://arxiv.org/abs/1702.02287v4","url_pdf":"http://arxiv.org/pdf/1702.02287v4.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":"name-disambiguation-in-anonymized-graphs","repo_url":"https://github.com/tangjianpku/LINE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"name-disambiguation-in-anonymized-graphs","repo_url":"https://github.com/baichuan/disambiguation_embedding","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"network-embedding","task_name":"Network Embedding"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1702.02287","atlas_url":"https://app.syntology.ai/?focus=1702.02287","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}