Implementation of Power Law Graph Transformer for Machine Translation and Representation Learning.
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Updated
Jul 19, 2021 - Python
Implementation of Power Law Graph Transformer for Machine Translation and Representation Learning.
Codebase of paper "Balancing structure and position information in Graph Transformer network with a learnable node embedding"
[AAAI'23] MulGT: Multi-task Graph-Transformer with Task-aware Knowledge Injection and Domain Knowledge-driven Pooling for Whole Slide Image Analysis
[MICCAI'23] HIGT: Hierarchical Interaction Graph-Transformer for Whole Slide Image Analysis
Code for our paper "Attending to Graph Transformers"
Protein Structure Transformer (PST): Endowing pretrained protein language models with structural knowledge
Code for VN-Solver: Vision-based Neural Solver for Combinatorial Optimization over Graphs
VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections, ICLR 2024
Test graph isomorphism with 1-WL for different graph classes and labelings
Official Pytorch implementation of NeuralWalker
Codebase for paper: "Improving GCN with Transformer layer in social-based items recommendation"
The Graph Representation Learning Framework developed by NS Lab @ CUK.
2021 AAAI Modular Graph Transformer Networks for Multi-Label Image Classification; Official GitHub: https://github.com/ReML-AI/MGTN
Scalable and privacy-enhanced graph generative models for benchmark graph neural networks
Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering
This repository reproduces the results in the paper "How expressive are transformers in spectral domain for graphs?"(published in TMLR)
DeepResponse: Large Scale Prediction of Cancer Cell Line Drug Response with Deep Learning Based Pharmacogenomic Modelling
Hop-Wise Graph Attention for Scalable and Generalizable Learning on Circuits
Triplet Graph Transformer
Community-aware Graph Transformer (CGT) is a novel Graph Transformer model that utilizes community structures to address node degree biases in message-passing mechanism and developed by NS Lab @ CUK based on pure PyTorch backend.
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