Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Graphs naturally appear in numerous application domains, ranging from social analysis,
bioinformatics to computer vision. The unique capability of graphs enables capturing the …

Abstract Graph Neural Networks (GNNs) are widely applied for graph anomaly detection. As
one of the key components for GNN design is to select a tailored spectral filter, we take the …

Graph neural networks (GNNs) have been proven to be effective in various network-related
tasks. Most existing GNNs usually exploit the low-frequency signals of node features, which …

Graph convolutional networks (GCNs) are a powerful deep learning approach for graph-
structured data. Recently, GCNs and subsequent variants have shown superior performance …

The pairwise interaction paradigm of graph machine learning has predominantly governed
the modelling of relational systems. However, graphs alone cannot capture the multi-level …

Graph neural networks (GNNs) have been widely used in representation learning on graphs
and achieved state-of-the-art performance in tasks such as node classification and link …

The richness in the content of various information networks such as social networks and
communication networks provides the unprecedented potential for learning high-quality …

Graph Convolutional Networks (GCNs) have been drawing significant attention with the
power of representation learning on graphs. Unlike Convolutional Neural Networks (CNNs) …

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