论文标题
学习标签初始化，以依赖时间谐波扩展
Learning Label Initialization for Time-Dependent Harmonic Extension
论文作者
论文摘要
图上的节点分类可以作为图表上的dirichlet问题进行表述，在该图上给出了标记节点处的信号，并且谐波扩展是在未标记的节点上完成的。本文考虑了图表上的Dirichlet问题的时间相关版本，并通过学习未标记的节点上的适当初始化向量来改善其解决方案。此外，我们表明改进的解决方案与用于节点分类的最新方法相当。最后，我们通过讨论参数t，专业和未来方向的重要性来结束本文。
Node classification on graphs can be formulated as the Dirichlet problem on graphs where the signal is given at the labeled nodes, and the harmonic extension is done on the unlabeled nodes. This paper considers a time-dependent version of the Dirichlet problem on graphs and shows how to improve its solution by learning the proper initialization vector on the unlabeled nodes. Further, we show that the improved solution is at par with state-of-the-art methods used for node classification. Finally, we conclude this paper by discussing the importance of parameter t, pros, and future directions.
