Add to ORKGThe Transformer architecture has gained growing attention in graph representation learning recently, as it naturally overcomes several limitations of graph neural networks (GNNs) by avoiding their strict structural inductive biases and instead only encoding the graph structure via positional encoding. Here, we show that the node representations generated by the Transformer with positional encoding do not necessarily capture structural similarity between them. To address this issue, we propose the Structure-Aware Transformer, a class of simple and flexible graph Transformers built upon a new self-attention mechanism. This new self-attention incorporates structural information into the original self-attention by extracting a subgraph represen...
Self-supervised learning methods became a popular approach for graph representation learning because...
(a) An input graph. (b) The Graph Convolutional Network. (c) Continuous representations learned for ...
Graph Contrastive Learning (GCL) recently has drawn much research interest for learning generalizabl...
We show that viewing graphs as sets of node features and incorporating structural and positional inf...
Transformer architectures have been applied to graph-specific data such as protein structure and sho...
Transformers have become widely used in modern models for various tasks such as natural language pro...
Recently, transformers have shown promising performance in learning graph representations. However, ...
Despite the great achievements of Graph Neural Networks (GNNs) in graph learning, conventional GNNs ...
Graph Neural Networks and Transformers are very powerful frameworks for learning machine learning ta...
We propose a novel positional encoding for learning graph on Transformer architecture. Existing appr...
Graph representation learning methods have attracted an increasing amount of attention in recent yea...
The graph neural network has received significant attention in recent years because of its unique ro...
The dominant graph-to-sequence transduction models employ graph neural networks for graph representa...
As more deep learning models are being applied in real-world applications, there is a growing need f...
Unsupervised Graph Representation Learning methods learn a numerical representation of the nodes in ...
Self-supervised learning methods became a popular approach for graph representation learning because...
(a) An input graph. (b) The Graph Convolutional Network. (c) Continuous representations learned for ...
Graph Contrastive Learning (GCL) recently has drawn much research interest for learning generalizabl...
We show that viewing graphs as sets of node features and incorporating structural and positional inf...
Transformer architectures have been applied to graph-specific data such as protein structure and sho...
Transformers have become widely used in modern models for various tasks such as natural language pro...
Recently, transformers have shown promising performance in learning graph representations. However, ...
Despite the great achievements of Graph Neural Networks (GNNs) in graph learning, conventional GNNs ...
Graph Neural Networks and Transformers are very powerful frameworks for learning machine learning ta...
We propose a novel positional encoding for learning graph on Transformer architecture. Existing appr...
Graph representation learning methods have attracted an increasing amount of attention in recent yea...
The graph neural network has received significant attention in recent years because of its unique ro...
The dominant graph-to-sequence transduction models employ graph neural networks for graph representa...
As more deep learning models are being applied in real-world applications, there is a growing need f...
Unsupervised Graph Representation Learning methods learn a numerical representation of the nodes in ...
Self-supervised learning methods became a popular approach for graph representation learning because...
(a) An input graph. (b) The Graph Convolutional Network. (c) Continuous representations learned for ...
Graph Contrastive Learning (GCL) recently has drawn much research interest for learning generalizabl...