Detecting emerging academic field by network algorithm.
| Project/Area Number |
16K16167
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Library and information science/Humanistic social informatics
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| Research Institution | The University of Tokyo |
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Principal Investigator |
ASATANI KIMITAKA 東京大学, 大学院工学系研究科(工学部), 特任研究員 (70770395)
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| Project Period (FY) |
2016-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2017: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2016: ¥2,600,000 (Direct Cost: ¥2,000,000、Indirect Cost: ¥600,000)
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| Keywords | 複雑ネットワーク / 書誌情報 / ネットワーク / 表現学習 / フォーサイト |
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| Outline of Final Research Achievements |
In this research, we developed the method that detect academic trend from citation network structure. It is important to grasp trend of an academic field for the making science foresight and planning strategy of R&D. In previous studies, several network features and information retrieval methods have been proposed to elucidate the structure of citation networks and to detect important nodes. However, it is difficult to retrieve information related to trends in an academic field and to detect cutting-edge areas from the citation network. We propose a novel framework that detects the trend as the growth direction of a citation network using network representation learning. On several datasets, we confirm the existence of trends by observing that an academic field grows in a specific direction linearly in latent space. Moreover, we confirm that the detected direction can be used for future citation prediction with higher accuracy compared to existing method.
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Report
(3 results)
Research Products
(8 results)
