This paper discusses a special kind of a simple yet possibly powerful algorithm, called single-kernel Gradraker (SKG), which is an adaptive learning method predicting unknown nodal values in a network using known nodal values and the network structure. We aim to find out how to configure the special kind of the model in applying the algorithm. To be more specific, we focus on SKG with a Gaussian kernel and specify how to find a suitable variance for the kernel. To do so, we introduce two variables with which we are able to set up requirements on the variance of the Gaussian kernel to achieve (near-) optimal performance and can better understand how SKG works. Our contribution is that we introduce two variables as analysis tools, illustrate how predictions will be affected under different Gaussian kernels, and provide an algorithm finding a suitable Gaussian kernel for SKG with knowledge about the training network. Simulation results on real datasets are provided.

中文翻译：

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图信号自适应学习方法的高斯核方差


本文讨论了一种特殊的简单但可能强大的算法，称为单核 Gradraker (SKG)，它是一种自适应学习方法，使用已知节点值和网络结构来预测网络中的未知节点值。我们的目标是找出在应用算法时如何配置特殊类型的模型。更具体地说，我们关注具有高斯核的 SKG，并指定如何为核找到合适的方差。为此，我们引入了两个变量，通过它们我们可以设置高斯核方差的要求，以实现（接近）最佳性能，并可以更好地理解 SKG 的工作原理。我们的贡献是引入两个变量作为分析工具，说明在不同高斯核下预测将如何受到影响，并提供一种算法，利用有关训练网络的知识为 SKG 找到合适的高斯核。提供了真实数据集的模拟结果。