A Transfer Learning based framework for Link's Role Discovery
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- LIU Shu
- Univ. of Tokyo
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- KIKUTA Shumpei
- Univ. of Tokyo
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- TORIUMI Fujio
- Univ. of Tokyo
Bibliographic Information
- Other Title
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- 転移学習に用いたリンクの役割発見
Abstract
<p>This paper aims to provide a framework for link's role discovery by using supervised information. The framework includes graph transformation, representation learning, transfer learning and role assignment. We use Edge-dual graph to regard links as nodes, struc2vec to gain links representation, adversarial learning model to transfer the target domain to the source domain to assign the roles for the target network's links. We show our proposed framework with better accuracy compared with existed method by a series of experiments on adjusted barbell graphs. Future work includes wider applications on other topology networks and real-world networks as well, and improvement on accuracy with bigger difference between the source network and the target network by updating the components used in the proposed framework.</p>
Journal
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- Proceedings of the Annual Conference of JSAI
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Proceedings of the Annual Conference of JSAI JSAI2020 (0), 2E6GS503-2E6GS503, 2020
The Japanese Society for Artificial Intelligence
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Details 詳細情報について
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- CRID
- 1390848250119498240
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- NII Article ID
- 130007856886
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- Text Lang
- ja
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- Data Source
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- JaLC
- CiNii Articles
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- Abstract License Flag
- Disallowed