Popular graph neural networks implement convolution operations on graphs based on
polynomial spectral filters. In this paper, we propose a novel graph convolutional layer …

Graph Neural Networks have recently become a prevailing paradigm for various high-impact
graph analytical problems. Existing efforts can be mainly categorized as spectral-based and …

Many representative graph neural networks, $ eg $, GPR-GNN and ChebNet, approximate
graph convolutions with graph spectral filters. However, existing work either applies …

Filters are fundamental in extracting information from data. For time series and image data
that reside on Euclidean domains, filters are the crux of many signal processing and …

Spectral graph convolutional neural networks (CNNs) require approximation to the
convolution to alleviate the computational complexity, resulting in performance loss. This …

Recently, many researchers have been focusing on the definition of neural networks for
graphs. The basic component for many of these approaches remains the graph convolution …

Abstract Graph Convolutional Neural Networks (Graph CNNs) are generalizations of
classical CNNs to handle graph data such as molecular data, point could and social …

Network data can be conveniently modeled as a graph signal, where data values are
assigned to nodes of a graph that describes the underlying network topology. Successful …

This paper aims at revisiting Graph Convolutional Neural Networks by bridging the gap
between spectral and spatial design of graph convolutions. We theoretically demonstrate …

The rise of graph-structured data such as social networks, regulatory networks, citation
graphs, and functional brain networks, in combination with resounding success of deep …