{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T13:08:24Z","timestamp":1775912904731,"version":"3.50.1"},"reference-count":38,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T00:00:00Z","timestamp":1646784000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"crossref","award":["61872446 and U19B2024"],"award-info":[{"award-number":["61872446 and U19B2024"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"NSF of Hunan Province","award":["2019JJ20024"],"award-info":[{"award-number":["2019JJ20024"]}]},{"name":"Science and Technology Innovation Program of Hunan Province","award":["2020RC4046"],"award-info":[{"award-number":["2020RC4046"]}]},{"name":"Hybrid Intelligence Center"},{"name":"Dutch Ministry of Education, Culture and Science through the Netherlands Organisation for Scientific Research"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2022,10,31]]},"abstract":"<jats:p>\n            Content representation is a fundamental task in information retrieval. Representation learning is aimed at capturing features of an information object in a low-dimensional space. Most research on representation learning for heterogeneous information networks (HINs) focuses on static HINs. In practice, however, networks are dynamic and subject to constant change. In this article, we propose a novel and scalable representation learning model,\n            <jats:sans-serif>M-DHIN<\/jats:sans-serif>\n            , to explore the evolution of a dynamic HIN. We regard a dynamic HIN as a series of snapshots with different time stamps. We first use a static embedding method to learn the initial embeddings of a dynamic HIN at the first time stamp. We describe the features of the initial HIN via metagraphs, which retains more structural and semantic information than traditional path-oriented static models. We also adopt a complex embedding scheme to better distinguish between symmetric and asymmetric metagraphs. Unlike traditional models that process an entire network at each time stamp, we build a so-called\n            <jats:italic>change dataset<\/jats:italic>\n            that only includes nodes involved in a triadic closure or opening process, as well as newly added or deleted nodes. Then, we utilize the above metagraph-based mechanism to train on the change dataset. As a result of this setup,\n            <jats:sans-serif>M-DHIN<\/jats:sans-serif>\n            is scalable to large dynamic HINs since it only needs to model the entire HIN once while only the changed parts need to be processed over time. Existing dynamic embedding models only express the existing snapshots and cannot predict the future network structure. To equip\n            <jats:sans-serif>M-DHIN<\/jats:sans-serif>\n            with this ability, we introduce an LSTM-based deep autoencoder model that processes the evolution of the graph via an LSTM encoder and outputs the predicted graph. Finally, we evaluate the proposed model,\n            <jats:sans-serif>M-DHIN<\/jats:sans-serif>\n            , on real-life datasets and demonstrate that it significantly and consistently outperforms state-of-the-art models.\n          <\/jats:p>","DOI":"10.1145\/3485189","type":"journal-article","created":{"date-parts":[[2022,3,10]],"date-time":"2022-03-10T04:49:38Z","timestamp":1646887778000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["Scalable Representation Learning for Dynamic Heterogeneous Information Networks via Metagraphs"],"prefix":"10.1145","volume":"40","author":[{"given":"Yang","family":"Fang","sequence":"first","affiliation":[{"name":"National University of Defense Technology, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiang","family":"Zhao","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peixin","family":"Huang","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weidong","family":"Xiao","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1086-0202","authenticated-orcid":false,"given":"Maarten","family":"de Rijke","sequence":"additional","affiliation":[{"name":"University of Amsterdam Amsterdam, Amsterdam, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,3,9]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/2488388.2488393"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-014-0365-y"},{"key":"e_1_3_3_4_2","first-page":"585","volume-title":"Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic] (NIPS\u201901)","author":"Belkin Mikhail","year":"2001","unstructured":"Mikhail Belkin and Partha Niyogi. 2001. Laplacian eigenmaps and spectral techniques for embedding and clustering. In Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic] (NIPS\u201901). 585\u2013591. http:\/\/papers.nips.cc\/paper\/1961-laplacian-eigenmaps-and-spectral-techniques-for-embedding-and-clustering."},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331273"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806512"},{"key":"e_1_3_3_7_2","volume-title":"Multidimensional Scaling","author":"Cox Michael A. A.","year":"2008","unstructured":"Michael A. A. Cox and Trevor F. Cox. 2008. Multidimensional Scaling. 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