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Found 8 papers, 1 papers with code
Diversified Node Sampling based Hierarchical Transformer Pooling for Graph Representation Learning
Graph pooling methods have been widely used on downsampling graphs, achieving impressive results on multiple graph-level tasks like graph classification and graph generation.
SignGT: Signed Attention-based Graph Transformer for Graph Representation Learning
In this way, SignGT could learn informative node representations from both long-range dependencies and local topology information.
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Node Classification
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Tokenized Graph Transformer with Neighborhood Augmentation for Node Classification in Large Graphs
Graph Transformers, emerging as a new architecture for graph representation learning, suffer from the quadratic complexity on the number of nodes when handling large graphs.
Adaptive Multi-Neighborhood Attention based Transformer for Graph Representation Learning
MNA-GT further employs an attention layer to learn the importance of different attention kernels to enable the model to adaptively capture the graph structural information for different nodes.
Neighborhood Convolutional Network: A New Paradigm of Graph Neural Networks for Node Classification
The decoupled Graph Convolutional Network (GCN), a recent development of GCN that decouples the neighborhood aggregation and feature transformation in each convolutional layer, has shown promising performance for graph representation learning.
Propagation with Adaptive Mask then Training for Node Classification on Attributed Networks
However, they follow the traditional structure-aware propagation strategy of GCNs, making it hard to capture the attribute correlation of nodes and sensitive to the structure noise described by edges whose two endpoints belong to different categories.
NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs
In this work, we observe that existing graph Transformers treat nodes as independent tokens and construct a single long sequence composed of all node tokens so as to train the Transformer model, causing it hard to scale to large graphs due to the quadratic complexity on the number of nodes for the self-attention computation.
Multi-Output Convolution Spectral Mixture for Gaussian Processes
The resulting kernel is called Multi-Output Convolution Spectral Mixture (MOCSM) kernel.
