We propose a kernel regression method to predict a target signal lying over a graph when
an input observation is given. The input and the output could be two different physical …

Abstract Graph Neural Networks (GNNs) are a powerful class of machine learning models
with applications in recommender systems, drug discovery, social network analysis, and …

We propose a kernel regression method to predict a target signal lying over a graph when
an input observation is given. The input and the output could be two different physical …

Kernel and linear regression have been recently explored in the prediction of graph signals
as the output, given arbitrary input signals that are agnostic to the graph. In many real-world …

In this paper we consider the problem of predicting a signal y_t, defined across the N nodes
of a fixed graph, over a series of $ T $ regularly sampled time points. At each moment the …

Learning on graph data is a application of machine learning and a significant amount of
recent research efforts have been devoted to it. Applications on this area is seen on …