Enabling Automatic Graph Learning Pipelines with Limited Human Knowledge

在人类知识有限的情况下启用自动图学习管道

• 批准号: FT210100097
• 负责人: Prof Shirui Pan
• 金额: $ 56.6万
• 依托单位: Griffith University
• 依托单位国家: 澳大利亚
• 项目类别: ARC Future Fellowships
• 财政年份: 2022
• 资助国家: 澳大利亚
• 起止时间: 2022-01-10 至 2026-01-09
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/FT210100097
• 关键词: Enabling; Automatic; Graph; Learning; Pipelines

This project aims to develop an automatic graph learning system for complex graph data analysis. Machine learning for graph data commonly requires significant human knowledge from both domain professionals as well as algorithm experts, rendering existing systems ineffective and unexplainable. This project expects to design novel graph learning techniques which automatically infer graph relations, learn graph models, adapts existing knowledge to new domains, and provide explanations to the graph learning system. The research results should provide benefit to governments and businesses in many critical applications, such as bioassay activity prediction, credit assessment, and drug discovery and vaccine development in response to the pandemic.

本计画旨在开发一个自动化的图形学习系统，以进行复杂图形资料的分析。图形数据的机器学习通常需要来自领域专业人员和算法专家的大量人类知识，这使得现有系统无效且无法解释。该项目期望设计新颖的图学习技术，自动推断图关系，学习图模型，使现有知识适应新的领域，并为图学习系统提供解释。这些研究成果将在许多关键应用中为政府和企业带来益处，例如生物测定活性预测、信用评估以及应对大流行的药物发现和疫苗开发。