Graph Transformer Architecture. Source code for "A Generalization of Transformer Networks to Graphs", DLG-AAAI'21.
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Updated
Jul 27, 2021 - Python
Graph Transformer Architecture. Source code for "A Generalization of Transformer Networks to Graphs", DLG-AAAI'21.
Recipe for a General, Powerful, Scalable Graph Transformer
Universal Graph Transformer Self-Attention Networks (TheWebConf WWW 2022) (Pytorch and Tensorflow)
Papers about graph transformers.
Deep learning toolkit for Drug Design with Pareto-based Multi-Objective optimization in Polypharmacology
Official Pytorch code for Structure-Aware Transformer.
Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations), ICLR 2022
[AAAI2023] A PyTorch implementation of PDFormer: Propagation Delay-aware Dynamic Long-range Transformer for Traffic Flow Prediction.
The official implementation for ICLR23 spotlight paper "DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion"
The official implementation of NeurIPS22 spotlight paper "NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification"
Code for AAAI2020 paper "Graph Transformer for Graph-to-Sequence Learning"
[ICLR 2023] One Transformer Can Understand Both 2D & 3D Molecular Data (official implementation)
Official Code Repository for the paper "Accurate Learning of Graph Representations with Graph Multiset Pooling" (ICLR 2021)
Long Range Graph Benchmark, NeurIPS 2022 Track on D&B
SignNet and BasisNet
[SIGIR'2023] "GFormer: Graph Transformer for Recommendation"
Implementation for the paper: Representation Learning on Knowledge Graphs for Node Importance Estimation
Video Graph Transformer for Video Question Answering (ECCV'22)
Code for our paper "Attending to Graph Transformers"
MANDO-HGT is a framework for detecting smart contract vulnerabilities. Given either in source code or bytecode forms, MANDO-HGT adapts heterogeneous graph transformers with customized meta relations for graph nodes and edges to learn their embeddings and train classifiers for detecting various vulnerability types in the contracts' nodes and graphs.
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