{"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/a-capsule-network-based-model-for-learning","title":"A Capsule Network-based Model for Learning Node Embeddings","arxiv_id":"1911.04822","date":"2019-11-12","proceeding":null,"authors":["Dai Quoc Nguyen","Tu Dinh Nguyen","Dat Quoc Nguyen","Dinh Phung"],"abstract":"In this paper, we focus on learning low-dimensional embeddings for nodes in graph-structured data. To achieve this, we propose Caps2NE -- a new unsupervised embedding model leveraging a network of two capsule layers. Caps2NE induces a routing process to aggregate feature vectors of context neighbors of a given target node at the first capsule layer, then feed these features into the second capsule layer to infer a plausible embedding for the target node. Experimental results show that our proposed Caps2NE obtains state-of-the-art performances on benchmark datasets for the node classification task. Our code is available at: \\url{https://github.com/daiquocnguyen/Caps2NE}.","url_abs":"https://arxiv.org/abs/1911.04822v2","url_pdf":"https://arxiv.org/pdf/1911.04822v2.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":"a-capsule-network-based-model-for-learning","repo_url":"https://github.com/daiquocnguyen/Caps2NE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"node-classification","task_name":"Node Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/node-classification-on-cora","task":"Node Classification","dataset":"Cora","model":"Caps2NE","rank_in_archive_order":65,"of":73,"metrics":{"Accuracy":"80.53%"},"uses_additional_data":false},{"leaderboard":"/sota/node-classification-on-pubmed","task":"Node Classification","dataset":"Pubmed","model":"Caps2NE","rank_in_archive_order":55,"of":70,"metrics":{"Accuracy":"78.45%"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}