{"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/easing-embedding-learning-by-comprehensive","title":"Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks","arxiv_id":"1807.03490","date":"2018-07-10","proceeding":null,"authors":["Yu Shi","Qi Zhu","Fang Guo","Chao Zhang","Jiawei Han"],"abstract":"Heterogeneous information networks (HINs) are ubiquitous in real-world\napplications. In the meantime, network embedding has emerged as a convenient\ntool to mine and learn from networked data. As a result, it is of interest to\ndevelop HIN embedding methods. However, the heterogeneity in HINs introduces\nnot only rich information but also potentially incompatible semantics, which\nposes special challenges to embedding learning in HINs. With the intention to\npreserve the rich yet potentially incompatible information in HIN embedding, we\npropose to study the problem of comprehensive transcription of heterogeneous\ninformation networks. The comprehensive transcription of HINs also provides an\neasy-to-use approach to unleash the power of HINs, since it requires no\nadditional supervision, expertise, or feature engineering. To cope with the\nchallenges in the comprehensive transcription of HINs, we propose the HEER\nalgorithm, which embeds HINs via edge representations that are further coupled\nwith properly-learned heterogeneous metrics. To corroborate the efficacy of\nHEER, we conducted experiments on two large-scale real-words datasets with an\nedge reconstruction task and multiple case studies. Experiment results\ndemonstrate the effectiveness of the proposed HEER model and the utility of\nedge representations and heterogeneous metrics. The code and data are available\nat https://github.com/GentleZhu/HEER.","url_abs":"http://arxiv.org/abs/1807.03490v1","url_pdf":"http://arxiv.org/pdf/1807.03490v1.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":"easing-embedding-learning-by-comprehensive","repo_url":"https://github.com/GentleZhu/HEER","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"network-embedding","task_name":"Network Embedding"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.03490","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.03490"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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