{"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/metapath2vec-scalable-representation-learning","title":"metapath2vec: Scalable Representation Learning for Heterogeneous Networks","arxiv_id":null,"date":"2017-08-01","proceeding":"KDD 17 2017 8","authors":["Yuxiao Dong","Nitesh Vijay Chawla","Ananthram Swami"],"abstract":"We study the problem of representation learning in heterogeneous networks. Its unique challenges come from the existence of multiple types of nodes and links, which limit the feasibility of the conventional network embedding techniques. We develop two scalable representation learning models, namely metapath2vec and metapath2vec++. The metapath2vec model formalizes meta-path-based random walks to construct the heterogeneous neighborhood of a node and then leverages a heterogeneous skip-gram model to perform node embeddings. The metapath2vec++ model further enables the simultaneous modeling of structural and semantic correlations in heterogeneous networks. Extensive experiments show that metapath2vec and metapath2vec++ are able to not only outperform state-of-the-art embedding models in various heterogeneous network mining tasks, such as node classification, clustering, and similarity search, but also discern the structural and semantic correlations between diverse network objects.","url_abs":"https://dl.acm.org/doi/10.1145/3097983.3098036","url_pdf":"https://dl.acm.org/doi/pdf/10.1145/3097983.3098036?download=true","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":"metapath2vec-scalable-representation-learning","repo_url":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/metapath2vec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"link-prediction","task_name":"Link Prediction"},{"task_slug":"network-embedding","task_name":"Network Embedding"},{"task_slug":"node-classification","task_name":"Node Classification"},{"task_slug":"node-property-prediction","task_name":"Node Property Prediction"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[{"method_slug":"metapath2vec","method_name":"metapath2vec"}],"datasets_introduced":[],"methods_introduced":[{"slug":"metapath2vec","name":"metapath2vec","full_name":"metapath2vec"}],"results":[{"leaderboard":"/sota/link-prediction-on-movielens-25m","task":"Link Prediction","dataset":"MovieLens 25M","model":"metapath2vec","rank_in_archive_order":5,"of":7,"metrics":{"Hits@10":"0.7956","nDCG@10":"0.5051"},"uses_additional_data":false},{"leaderboard":"/sota/link-prediction-on-yelp","task":"Link Prediction","dataset":"Yelp","model":"Metapath2Vec","rank_in_archive_order":7,"of":9,"metrics":{"HR@10":"0.6307","nDCG@10":"0.402"},"uses_additional_data":false},{"leaderboard":"/sota/node-property-prediction-on-ogbn-mag","task":"Node Property Prediction","dataset":"ogbn-mag","model":"MetaPath2vec","rank_in_archive_order":36,"of":39,"metrics":{"Ext. data":"No","Number of params":"94479069","Test Accuracy":"0.3544 ± 0.0036","Validation Accuracy":"0.3506 ± 0.0017"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}