{"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/column-networks-for-collective-classification","title":"Column Networks for Collective Classification","arxiv_id":"1609.04508","date":"2016-09-15","proceeding":null,"authors":["Trang Pham","Truyen Tran","Dinh Phung","Svetha Venkatesh"],"abstract":"Relational learning deals with data that are characterized by relational\nstructures. An important task is collective classification, which is to jointly\nclassify networked objects. While it holds a great promise to produce a better\naccuracy than non-collective classifiers, collective classification is\ncomputational challenging and has not leveraged on the recent breakthroughs of\ndeep learning. We present Column Network (CLN), a novel deep learning model for\ncollective classification in multi-relational domains. CLN has many desirable\ntheoretical properties: (i) it encodes multi-relations between any two\ninstances; (ii) it is deep and compact, allowing complex functions to be\napproximated at the network level with a small set of free parameters; (iii)\nlocal and relational features are learned simultaneously; (iv) long-range,\nhigher-order dependencies between instances are supported naturally; and (v)\ncrucially, learning and inference are efficient, linear in the size of the\nnetwork and the number of relations. We evaluate CLN on multiple real-world\napplications: (a) delay prediction in software projects, (b) PubMed Diabetes\npublication classification and (c) film genre classification. In all\napplications, CLN demonstrates a higher accuracy than state-of-the-art rivals.","url_abs":"http://arxiv.org/abs/1609.04508v2","url_pdf":"http://arxiv.org/pdf/1609.04508v2.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":"column-networks-for-collective-classification","repo_url":"https://github.com/trangptm/Column_networks","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"genre-classification","task_name":"Genre classification"},{"task_slug":"relational-reasoning","task_name":"Relational Reasoning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1609.04508","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}