Bipartite Graph Embedding: As A Unified Framework

2020 Fiscal Year Final Research Report

• Project/Area Number: 19K20352
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 61030:Intelligent informatics-related
• Research Institution: National Institute of Advanced Industrial Science and Technology
• Principal Investigator: LIU XIN 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)
• Project Period (FY): 2019-04-01 – 2021-03-31
• Keywords: Graph embedding / Graph neural network / Bipartite graph / Heterogeneous graph / Knowledge graph / Node classification / Node ranking

Outline of Final Research Achievements

The outcomes of the project involve both the research methods as well as open-source softwares. We have proposed embedding approaches for different graphs, including the unipartite graphs, the bipartite graphs, the heterogeneous knowledge graphs, the attributed graphs, and the multigraphs. We also have successfully applied our approaches to real-world applications, including forecasting ambulance demand, citation recommendation, and brain disorder diagnosis.

Free Research Field

知能情報学関連

Academic Significance and Societal Importance of the Research Achievements

このプロジェクトでは、様々なグラフで、グラフ埋め込みの共通の本質を明らかにします。これまでに、単部グラフ、二部グラフグラフ、異種頂点グラフ、ナレッジグラフ、属性付きグラフ、マルチグラフなど、さまざまなグラフに対する埋め込み手法を提案してきました。さまざまなグラフを研究することは、実世界の複雑なシステムを適切に表現し、幅広い応用につながるため、科学的に意義があります。