Learning directed acyclic graph (DAG) that describes the causality of
observed data is a very challenging but important task. Due to the limited
quantity and quality of observed data, and non-identifiability of causal graph,
it is almost impossible to infer a single precise DAG. Some methods approximate
the posterior distribution of DAGs to explore the DAG s
本研究探讨了动态图数据上节点特征生成机制的学习问题，并提出了一种连续得分优化方法，名为 GraphNOTEARS，该方法有效地刻画了节点特征生成过程中的同时关系和时滞交互关系之间的 DAG 结构，可以在一个简洁的方式下展现特征生成过程。实验表明，该算法在模拟数据方面表现优异，并能够学习到来自真实世界数据集的节点之间的联系。