An attempt at demystifying graph deep learning
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Updated
Oct 30, 2021 - HTML
An attempt at demystifying graph deep learning
Final assignment of EE226 course in SJTU by Group 12
Non markovian extension to the graph edit network model proposed by Paassen et al.
Tumor2Graph: a novel Overall-Tumor-Profile-derived virtual graph deep learning for predicting tumor typing and subtyping.
Source code and data of the paper entitled "iACP-GCR: Identifying multi-target anticancer compounds using multitask learning on graph convolutional residual neural networks"
A repo for baseline of graph pooling.
Deep Learning with Graph Representation of Bio-Molecules to estimate physical Properties
NLP - Semantic Role Labeling using GCN, Bert and Biaffine Attention Layer. Developed in Pytorch
Antibiotic discovery using graph deep learning, with Chemprop.
ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees (WSDM 2024)
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
Android Malware Detection with Graph Convolutional Networks using Function Call Graph and its Derivatives.
GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation (USENIX Security '23)
Locally Private Graph Neural Networks (ACM CCS 2021)
Bayesian Graph Neural Networks with Adaptive Connection Sampling - Pytorch
Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations), ICLR 2022
Universal Graph Transformer Self-Attention Networks (TheWebConf WWW 2022) (Pytorch and Tensorflow)
Graph Transformer Architecture. Source code for "A Generalization of Transformer Networks to Graphs", DLG-AAAI'21.
Graph Neural Networks with Keras and Tensorflow 2.
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