Research in graph signal processing (GSP) aims to develop tools for processing data
defined on irregular graph domains. In this paper, we first provide an overview of core ideas …

Deep learning methods have achieved great success in analyzing traditional data such as
texts, sounds, images and videos. More and more research works are carrying out to extend …

Geometric deep learning is an umbrella term for emerging techniques attempting to
generalize (structured) deep neural models to non-Euclidean domains, such as graphs and …

Deep learning has achieved a remarkable performance breakthrough in several fields, most
notably in speech recognition, natural language processing, and computer vision. In …

Network topology inference is a significant problem in network science. Most graph signal
processing (GSP) efforts to date assume that the underlying network is known and then …

Nodes residing in different parts of a graph can have similar structural roles within their local
network topology. The identification of such roles provides key insight into the organization …

In this paper, we study the problem of semantic annotation on 3D models that are
represented as shape graphs. A functional view is taken to represent localized information …

Feature descriptors play a crucial role in a wide range of geometry analysis and processing
applications, including shape correspondence, retrieval, and segmentation. In this paper, we …

Polynomial filters, a kind of Graph Neural Networks, typically use a predetermined
polynomial basis and learn the coefficients from the training data. It has been observed that …

The construction of a meaningful graph plays a crucial role in the success of many graph-
based representations and algorithms for handling structured data, especially in the …