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Specformer: Spectral graph neural networks meet transformers
Spectral graph neural networks (GNNs) learn graph representations via spectral-domain
graph convolutions. However, most existing spectral graph filters are scalar-to-scalar …
graph convolutions. However, most existing spectral graph filters are scalar-to-scalar …
Graph convolutional kernel machine versus graph convolutional networks
Graph convolutional networks (GCN) with one or two hidden layers have been widely used
in handling graph data that are prevalent in various disciplines. Many studies showed that …
in handling graph data that are prevalent in various disciplines. Many studies showed that …
Pc-conv: Unifying homophily and heterophily with two-fold filtering
Recently, many carefully designed graph representation learning methods have achieved
impressive performance on either strong heterophilic or homophilic graphs, but not both …
impressive performance on either strong heterophilic or homophilic graphs, but not both …
Energy transformer
Our work combines aspects of three promising paradigms in machine learning, namely,
attention mechanism, energy-based models, and associative memory. Attention is the power …
attention mechanism, energy-based models, and associative memory. Attention is the power …
A survey on spectral graph neural networks
Graph neural networks (GNNs) have attracted considerable attention from the research
community. It is well established that GNNs are usually roughly divided into spatial and …
community. It is well established that GNNs are usually roughly divided into spatial and …
Bridging the gap between spatial and spectral domains: A unified framework for graph neural networks
Deep learning's performance has been extensively recognized recently. Graph neural
networks (GNNs) are designed to deal with graph-structural data that classical deep …
networks (GNNs) are designed to deal with graph-structural data that classical deep …
Towards effective and general graph unlearning via mutual evolution
With the rapid advancement of AI applications, the growing needs for data privacy and
model robustness have highlighted the importance of machine unlearning, especially in …
model robustness have highlighted the importance of machine unlearning, especially in …
Multi-scale sampling attention graph convolutional networks for skeleton-based action recognition
Skeleton-based action recognition has attracted increasing interest in recent years. With the
flexibility of modeling long-range dependency of joints, the self-attention module has served …
flexibility of modeling long-range dependency of joints, the self-attention module has served …
Partitioning message passing for graph fraud detection
Label imbalance and homophily-heterophily mixture are the fundamental problems
encountered when applying Graph Neural Networks (GNNs) to Graph Fraud Detection …
encountered when applying Graph Neural Networks (GNNs) to Graph Fraud Detection …
Multiresolution graph transformers and wavelet positional encoding for learning long-range and hierarchical structures
Contemporary graph learning algorithms are not well-suited for large molecules since they
do not consider the hierarchical interactions among the atoms, which are essential to …
do not consider the hierarchical interactions among the atoms, which are essential to …