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Data"],"published-print":{"date-parts":[[2024,7,31]]},"abstract":"<jats:p>\n            Forecasting citations of scientific patents and publications is a crucial task for understanding the evolution and development of technological domains and for foresight into emerging technologies. By construing citations as a time series, the task can be cast into the domain of temporal point processes. Most existing work on forecasting with temporal point processes, both conventional and neural network-based, only performs single-step forecasting. In citation forecasting, however, the more salient goal is\n            <jats:italic>n<\/jats:italic>\n            -step forecasting: predicting the arrival of the next\n            <jats:italic>n<\/jats:italic>\n            citations. In this article, we propose Dynamic Multi-Context Attention Networks (DMA-Nets), a novel deep learning sequence-to-sequence (Seq2Seq) model with a novel hierarchical dynamic attention mechanism for long-term citation forecasting. Extensive experiments on two real-world datasets demonstrate that the proposed model learns better representations of conditional dependencies over historical sequences compared to state-of-the-art counterparts and thus achieves significant performance for citation predictions.\n          <\/jats:p>","DOI":"10.1145\/3649140","type":"journal-article","created":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T12:02:54Z","timestamp":1708689774000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Citation Forecasting with Multi-Context Attention-Aided Dependency Modeling"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9438-3038","authenticated-orcid":false,"given":"Taoran","family":"Ji","sequence":"first","affiliation":[{"name":"Department of Computer Science, Texas A&amp;M University, Corpus Christi, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8075-9866","authenticated-orcid":false,"given":"Nathan","family":"Self","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Virginia Tech, Arlington, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4307-9938","authenticated-orcid":false,"given":"Kaiqun","family":"Fu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, South Dakota State University, Brookings, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4112-9647","authenticated-orcid":false,"given":"Zhiqian","family":"Chen","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering Department, Mississippi State University, Starkville, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1821-9743","authenticated-orcid":false,"given":"Naren","family":"Ramakrishnan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Virginia Tech, Arlington, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3675-0199","authenticated-orcid":false,"given":"Chang-Tien","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Virginia Tech, Falls Church, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,4,12]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/489201a"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.0003-08.2008"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.respol.2008.02.005"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1002\/asi.21197"},{"key":"e_1_3_2_6_2","volume-title":"Standards for the Application of Bibliometrics in the Evaluation of Individual Researchers Working in the Natural Sciences","author":"Bornmann Lutz","year":"2013","unstructured":"Lutz Bornmann and Werner Marx. 2013. 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