Enabling Automatic Graph Learning Pipelines with Limited Human Knowledge

本项目旨在开发一个自动图学习系统，用于复杂的图数据分析。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，使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，将现有知识适应新领域，并为图学习系统提供解释。研究结果将在许多关键应用方面为政府和企业带来好处，例如生物测定活性预测、信用评估、药物发现和疫苗开发，以应对大流行。

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.