| 研究課題/領域番号 |
21K12042
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| 研究機関 | 国立研究開発法人産業技術総合研究所 |
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研究代表者 |
劉 欣 国立研究開発法人産業技術総合研究所, 情報・人間工学領域, 主任研究員 (20803935)
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| 研究期間 (年度) |
2021-04-01 – 2024-03-31
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| キーワード | Graph Neural Network / Graph Embedding / Node Classification / Dynamic Graph / Imbalanced Graph / Multi-Label Graph |
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| 研究実績の概要 |
We have achieved four main outcomes. a) We introduced several key design strategies for GNNs and proposed new models. b) We proposed single-level aggregation scheme for heterogeneous graph embedding, which addresses the down-weighting issue associated with the current bi-level scheme. c) We proposed data generation strategy and new GNN models for tackling the imbalanced graph node classification problem. d) We employed graph learning approach for predicting the donations on live streaming platforms.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
1: 当初の計画以上に進展している
理由
The current progress of the project is going well. There are no delays. The finished work has been published in prestigious conferences and journals. We are working on learning graphs in noisy environments.
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| 今後の研究の推進方策 |
We will focus on the following problems: 1) graph learning in noisy environments, 2) GNN for dynamic graphs and its applications.
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| 次年度使用額が生じた理由 |
Due to Covid-19, there is a small amount of leftover for travel expenses. It will be used in the next fiscal year.
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