Recently, adaptive graph convolutional network based traffic prediction methods, learning a
latent graph structure from traffic data via various attention-based mechanisms, have …

In this paper, we propose a method, based on graph signal processing, to optimize the
choice of k in k-nearest neighbor graphs (kNNGs). kNN is one of the most popular …

Learning graphs represented by-matrices via an-regularized Gaussian maximum-likelihood
method is a popular approach, but also one that poses computational challenges for large …

Graphs play a critical role in machine learning and data mining fields. The success of graph-
based machine learning algorithms highly depends on the quality of the underlying graphs …

Structure inference is an important task for network data processing and analysis in data
science. In recent years, quite a few approaches have been developed to learn the graph …

Graphs are nowadays ubiquitous in the fields of signal processing and machine learning. As
a tool used to express relationships between objects, graphs can be deployed to various …

We propose a simultaneous method of multimodal graph signal denoising and graph
learning. Since sensor networks distributed in space can capture multiple modalities of data …

In the quest to improve efficiency, interdependence and complexity are becoming defining
characteristics of modern complex networks representing engineered and natural systems …

A common challenge in applying graph machine learning methods is that the underlying
graph of a system is often unknown. Although different graph inference methods have been …

Learning meaningful graphs from data plays important roles in many data mining and
machine learning tasks, such as data representation and analysis, dimension reduction …