{"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/classification-using-link-prediction","title":"Classification Using Link Prediction","arxiv_id":"1810.00717","date":"2018-10-01","proceeding":null,"authors":["Seyed Amin Fadaee","Maryam Amir Haeri"],"abstract":"Link prediction in a graph is the problem of detecting the missing links that\nwould be formed in the near future. Using a graph representation of the data,\nwe can convert the problem of classification to the problem of link prediction\nwhich aims at finding the missing links between the unlabeled data (unlabeled\nnodes) and their classes. To our knowledge, despite the fact that numerous\nalgorithms use the graph representation of the data for classification, none\nare using link prediction as the heart of their classifying procedure. In this\nwork, we propose a novel algorithm called CULP (Classification Using Link\nPrediction) which uses a new structure namely Label Embedded Graph or LEG and a\nlink predictor to find the class of the unlabeled data. Different link\npredictors along with Compatibility Score - a new link predictor we proposed\nthat is designed specifically for our settings - has been used and showed\npromising results for classifying different datasets. This paper further\nimproved CULP by designing an extension called CULM which uses a majority vote\n(hence the M in the acronym) procedure with weights proportional to the\npredictions' confidences to use the predictive power of multiple link\npredictors and also exploits the low level features of the data. Extensive\nexperimental evaluations shows that both CULP and CULM are highly accurate and\ncompetitive with the cutting edge graph classifiers and general classifiers.","url_abs":"http://arxiv.org/abs/1810.00717v1","url_pdf":"http://arxiv.org/pdf/1810.00717v1.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":"classification-using-link-prediction","repo_url":"https://github.com/aminfadaee/culp","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"link-prediction","task_name":"Link Prediction"},{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1810.00717","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}