Training on large-scale graphs has achieved remarkable results in graph representation
learning, but its cost and storage have attracted increasing concerns. Existing graph …

Graph neural networks (GNNs) rely on graph convolutions to extract local features from
network data. These graph convolutions combine information from adjacent nodes using …

Graphons are infinite-dimensional objects that represent the limit of convergent sequences
of graphs as their number of nodes goes to infinity. This paper derives a theory of graphon …

Graph neural networks (GNNs) are composed of layers consisting of graph convolutions and
pointwise nonlinearities. Due to their invariance and stability properties, GNNs are provably …

This article studies approximate solutions to large-scale linear quadratic stochastic games
with homogeneous nodal dynamics' parameters and heterogeneous network couplings …

Recently, the paradigm of pre-training and fine-tuning graph neural networks has been
intensively studied and applied in a wide range of graph mining tasks. Its success is …

We propose a novel and principled method to learn a nonparametric graph model called
graphon, which is defined in an infinite-dimensional space and represents arbitrary-size …

Graph signal processing (GSP) has seen rapid developments in recent years. Since its
introduction around ten years ago, we have seen numerous new ideas and practical …

Graph signal processing is an emerging field which aims to model processes that exist on
the nodes of a network and are explained through diffusion over this structure. Graph signal …

With the wide application of spectral and algebraic theory in discrete signal processing
techniques in the field of graph signal processing, an increasing number of signal …