StellarGraph - Machine Learning on Graphs
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Updated
Aug 16, 2023 - Python
StellarGraph - Machine Learning on Graphs
Implementation of Learnable Aggregators for Graph Convolutional Networks in TensorFlow
POLYNOMIAL GRAPH CONVOLUTIONAL NETWORKS
Official implementation for the paper "Leveraging Dependency Grammar for Fine-Grained Offensive Language Detection using Graph Convolutional Networks" published at SocialNLP @ NAACL-HLT 2022.
Estimating human skeleton actions and poses from video and Kinect data using GCNs (graph convolutional networks)
PyTorch implementation of "Multi-hop Modulated Graph Convolutional Networks for 3D Human Pose Estimation", BMVC 2022
Baseline Experiments: Polynomial-Based Graph Convolutional Neural NetworksFor Graph Classification
🗺️ Problema multi-class relativo alla classificazione di attacchi sulla rete.
Source code and data of the paper entitled "iACP-GCR: Identifying multi-target anticancer compounds using multitask learning on graph convolutional residual neural networks"
Predicting the multi-trajectory evolution of multimodal brain connectivity.
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The code of SGCN for FMLC
learning GNNs
Implementation of Graph Node-Feature Convolution for Representation Learning in TensorFlow
[IEEE Transactions on Image Processing'2023] RS-Net: Regular Splitting Graph Network for 3D Human Pose Estimation
Calculating the nearest weather sensor for each traffic sensor and then merging the weather sensors' temporal data with the traffic sensors'.
[TNNLS 2022] Code for "Learning Disentangled Graph Convolutional Networks Locally and Globally"
A novel architecture and training strategy for graph neural networks (GNN). The proposed architecture, named as Autoencoder-Aided GNN (AA-GNN), compresses the convolutional features at multiple hidden layers, hinging on a novel end-to-end training procedure that learns different graph representations per each layer. As a result, the computationa…
Code for project on reasoning over multiple paths
The implementation of geometry-aware mesh convolutional network
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