| 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 / Geometric Learning / Graph Mining / Node Classification / Link Prediction |
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| 研究実績の概要 |
In terms of model evolution, we have proposed GNNs for heterogeneous graphs (multiple types of nodes and edges). Our approaches capture the rich semantic information and thus significantly outperform SOTA methods in link prediction, node classification, and node clustering tasks.
In terms of real-world application, we have proposed GNN-based approaches for various applications: a) We propose a GNN-based neural recommender method to solve the temporal dynamics problem. b) We propose Spatio-Temporal-Categorical GNN to solve the fine-grained incident prediction problem. c) We develop a citation network embedding algorithm to enhance the citation recommendation performance. d) We design a GNN-based model to handle cross-lingual text classification. In all of these applications, we demonstrate that our new approaches are superior to existing ones.
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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 top conferences and journals. We are working now on heterogeneous graph embedding with single-level aggregation instead of the traditional bi-level aggregation. We have finished some of the work and submitted the papers.
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| 今後の研究の推進方策 |
We will focus on the following problems: 1) Feature selection in GNNs, 2) GNN for imbalanced label classification, 3) GNN for temporal graphs.
We will also continue develop GNN-based approaches for solving various practical problems and applications.
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| 次年度使用額が生じた理由 |
We planned to buy a workstation and a graphics card in FY2021. However, the product release date of the fittest models was delayed to May/June 2022. We will buy them in FY2022.
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