Feb 7, 2022 · We propose the Structure-Aware Transformer, a class of simple and flexible graph Transformers built upon a new self-attention mechanism.

We present here the work most related to ours, namely the work stemming from message passing GNNs, positional representations on graphs, and graph Transformers.

Our structure-aware framework can leverage any existing GNN to extract the subgraph representation and systematically improve the peroformance relative to the ...

This work proposes the Structure-Aware Transformer, a class of simple and flexible graph Transformers built upon a new self-attention mechanism that ...

Our contribution φ Generalize self-attention to account for local structures by extracting a subgraph representation rooted at each node.

We present here the work most related to ours, namely the work stemming from message passing GNNs, positional representations on graphs, and graph Transformers.

Duration: 4:35
Posted: Aug 25, 2023

Sep 8, 2024 · In this work, we propose a novel architecture that combines Bidirectional Encoder Representations from Transformers with Graph Transformer (BERT ...

Transformers have achieved state-of-the-art performance in the fields of Computer. Vision (CV) and Natural Language Processing (NLP).

Apr 9, 2024 · The document proposes the Structure-Aware Transformer, a new type of graph neural network that incorporates structural information into self-attention.