{"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/meta-path-guided-embedding-for-similarity","title":"Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information Networks","arxiv_id":"1610.09769","date":"2016-10-31","proceeding":null,"authors":["Jingbo Shang","Meng Qu","Jialu Liu","Lance M. Kaplan","Jiawei Han","Jian Peng"],"abstract":"Most real-world data can be modeled as heterogeneous information networks\n(HINs) consisting of vertices of multiple types and their relationships. Search\nfor similar vertices of the same type in large HINs, such as bibliographic\nnetworks and business-review networks, is a fundamental problem with broad\napplications. Although similarity search in HINs has been studied previously,\nmost existing approaches neither explore rich semantic information embedded in\nthe network structures nor take user's preference as a guidance.\n  In this paper, we re-examine similarity search in HINs and propose a novel\nembedding-based framework. It models vertices as low-dimensional vectors to\nexplore network structure-embedded similarity. To accommodate user preferences\nat defining similarity semantics, our proposed framework, ESim, accepts\nuser-defined meta-paths as guidance to learn vertex vectors in a user-preferred\nembedding space. Moreover, an efficient and parallel sampling-based\noptimization algorithm has been developed to learn embeddings in large-scale\nHINs. Extensive experiments on real-world large-scale HINs demonstrate a\nsignificant improvement on the effectiveness of ESim over several\nstate-of-the-art algorithms as well as its scalability.","url_abs":"http://arxiv.org/abs/1610.09769v1","url_pdf":"http://arxiv.org/pdf/1610.09769v1.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":"meta-path-guided-embedding-for-similarity","repo_url":"https://github.com/shangjingbo1226/esim","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[],"methods":[{"method_slug":"esim","method_name":"ESIM"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1610.09769","atlas_url":"https://app.syntology.ai/?focus=1610.09769","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}