Graph Neural Networks with Keras and Tensorflow 2.
-
Updated
Jan 21, 2024 - Python
Graph Neural Networks with Keras and Tensorflow 2.
Graph Transformer Architecture. Source code for "A Generalization of Transformer Networks to Graphs", DLG-AAAI'21.
Universal Graph Transformer Self-Attention Networks (TheWebConf WWW 2022) (Pytorch and Tensorflow)
Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations), ICLR 2022
Bayesian Graph Neural Networks with Adaptive Connection Sampling - Pytorch
An unofficial implementation of Graph Transformer (Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification) - IJCAI 2021
slientruss3d : Python for stable truss analysis and optimization tool
NLP - Semantic Role Labeling using GCN, Bert and Biaffine Attention Layer. Developed in Pytorch
Tumor2Graph: a novel Overall-Tumor-Profile-derived virtual graph deep learning for predicting tumor typing and subtyping.
Antibiotic discovery using graph deep learning, with Chemprop.
A repo for baseline of graph pooling.
Graph Deep Learning Course Presentation - Action and Emotion Recognition by Graph Convolutional Network(GCN)
Source code and data of the paper entitled "iACP-GCR: Identifying multi-target anticancer compounds using multitask learning on graph convolutional residual neural networks"
Final assignment of EE226 course in SJTU by Group 12
Add a description, image, and links to the graph-deep-learning topic page so that developers can more easily learn about it.
To associate your repository with the graph-deep-learning topic, visit your repo's landing page and select "manage topics."