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GraphGallery is a gallery for benchmarking Graph Neural Networks, From InplusLab.
This is a PyTorch implementation of the GeniePath model in <GeniePath: Graph Neural Networks with Adaptive Receptive Paths> (https://arxiv.org/abs/1802.00910)
A Pytorch Implementation of “Modeling Local Geometric Structure of 3D Point Clouds using Geo-CNN”
PoseModel: An Open-Source Toolkit for Accurate and Robust Automated Behavioral Latent Embedding
A repo for baseline of graph pooling.
API wrapper for DolarPy
An implementation of the Graph Convolution Networks for the Cora, Citeseer, PubMed dataset.
Empirical Research over the possible advantages of pretraining a Graph Neural Network for Classification by using Link Prediction. We used GCN, GAT and GraphSAGE with minibatch generation. Done for the Learning From Networks course taught by professor Fabio Vandin at the University of Padova
Opto-fmri decoding of locus coeruleus firing patterns using Graph Neural Networks and the BrainGNN framework