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Knowl. Discov. Data"],"published-print":{"date-parts":[[2022,8,31]]},"abstract":"<jats:p>\n            Given a heterogeneous information network (HIN) H, a head node\n            <jats:italic>h<\/jats:italic>\n            , a meta-path P, and a tail node\n            <jats:italic>t<\/jats:italic>\n            , the meta-path prediction aims at predicting whether\n            <jats:italic>h<\/jats:italic>\n            can be linked to\n            <jats:italic>t<\/jats:italic>\n            by an instance of P. Most existing solutions either require predefined meta-paths, which limits their scalability to schema-rich HINs and long meta-paths, or do not aim at predicting the existence of an instance of P. To address these issues, in this article, we propose a novel prediction model, called ABLE, by exploiting the\n            <jats:underline>A<\/jats:underline>\n            ttention mechanism and\n            <jats:underline>B<\/jats:underline>\n            i\n            <jats:underline>L<\/jats:underline>\n            STM for\n            <jats:underline>E<\/jats:underline>\n            mbedding. Particularly, we present a concatenation node embedding method by considering the node types and a dynamic meta-path embedding method that carefully considers the importance and positions of edge types in the meta-paths by the Attention mechanism and BiLSTM model, respectively. A triplet embedding is then derived to complete the prediction. We conduct extensive experiments on four real datasets. The empirical results show that ABLE outperforms the state-of-the-art methods by up to 20% and 22% of improvement of AUC and AP scores, respectively.\n          <\/jats:p>","DOI":"10.1145\/3494558","type":"journal-article","created":{"date-parts":[[2022,1,8]],"date-time":"2022-01-08T20:51:00Z","timestamp":1641675060000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["ABLE: Meta-Path Prediction in Heterogeneous Information Networks"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6924-1169","authenticated-orcid":false,"given":"Chenji","family":"Huang","sequence":"first","affiliation":[{"name":"The University of New South Wales, UNSW Sydney, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5047-8593","authenticated-orcid":false,"given":"Yixiang","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Data Science, The Chinese University of Hong Kong, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2396-7225","authenticated-orcid":false,"given":"Xuemin","family":"Lin","sequence":"additional","affiliation":[{"name":"The University of New South Wales, UNSW Sydney, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3519-7013","authenticated-orcid":false,"given":"Xin","family":"Cao","sequence":"additional","affiliation":[{"name":"The University of New South Wales, UNSW Sydney, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6572-2600","authenticated-orcid":false,"given":"Wenjie","family":"Zhang","sequence":"additional","affiliation":[{"name":"The University of New South Wales, UNSW Sydney, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,1,8]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.279"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.5555\/2999792.2999923"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.5555\/3504035.3504294"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-1165"},{"key":"e_1_3_2_6_2","first-page":"2174","volume-title":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing","author":"Choi Eunsol","year":"2020","unstructured":"Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen Tau Yih, Yejin Choi, Percy Liang, and Luke Zettlemoyer. 2020. 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