graph-deep-learning
There are 21 repositories under graph-deep-learning topic.
graphdeeplearning/benchmarking-gnns
Repository for benchmarking graph neural networks
danielegrattarola/spektral
Graph Neural Networks with Keras and Tensorflow 2.
graphdeeplearning/graphtransformer
Graph Transformer Architecture. Source code for "A Generalization of Transformer Networks to Graphs", DLG-AAAI'21.
daiquocnguyen/Graph-Transformer
Universal Graph Transformer Self-Attention Networks (TheWebConf WWW 2022) (Pytorch and Tensorflow)
vijaydwivedi75/gnn-lspe
Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations), ICLR 2022
armanihm/GDC
Bayesian Graph Neural Networks with Adaptive Connection Sampling - Pytorch
sisaman/LPGNN
Locally Private Graph Neural Networks (ACM CCS 2021)
sisaman/GAP
GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation (USENIX Security '23)
vinayakakv/android-malware-detection
Android Malware Detection with Graph Convolutional Networks using Function Call Graph and its Derivatives.
willyfh/graph-transformer
An unofficial implementation of Graph Transformer (Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification) - IJCAI 2021
leo27945875/Python_Stable_3D_Truss_Analysis
slientruss3d : Python for stable truss analysis and optimization tool
andreabac3/NLP-Semantic-Role-Labeling
NLP - Semantic Role Labeling using GCN, Bert and Biaffine Attention Layer. Developed in Pytorch
ericmjl/graph-deep-learning-demystified
An attempt at demystifying graph deep learning
Eurus-Holmes/Tumor2Graph
Tumor2Graph: a novel Overall-Tumor-Profile-derived virtual graph deep learning for predicting tumor typing and subtyping.
sisaman/ProGAP
ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees (WSDM 2024)
KarolinaGustavsson/Antibiotics_Chemprop
Antibiotic discovery using graph deep learning, with Chemprop.
codingClaire/GraphPoolingGarden
A repo for baseline of graph pooling.
indropal/GraphDLBioMolecules
Deep Learning with Graph Representation of Bio-Molecules to estimate physical Properties
felixboelter/Non-Markovian-Graph-Edit-Networks
Non markovian extension to the graph edit network model proposed by Paassen et al.
mldlproject/2023-iACP-GCR
Source code and data of the paper entitled "iACP-GCR: Identifying multi-target anticancer compounds using multitask learning on graph convolutional residual neural networks"
SkyRiver-2000/EE226-Final-Project
Final assignment of EE226 course in SJTU by Group 12