PyTorch implementation of Graph Attention Networks
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Updated
Sep 10, 2019 - Python
PyTorch implementation of Graph Attention Networks
GraphSAGE and GAT for link prediction.
Keyphrase extraction using graph convolution
Modeling Extent-of-Texture Information for Ground Terrain Recognition
Heterogeneous Graph Neural Network
A deep learning library to rank protein complexes using graph neural networks
A Drug Metabolite & Toxicity Property Predictor Based on Graph Neural Network
Graph Attention Networks (GATs)
Dense and Sparse Implementation of GAT written by PyTorch
GATor is a Graph Attention Network for object detection with relational reasoning
The GitHub repository for the paper "Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations"
This repository hosts the scripts and some of the pre-trained models presented in out paper "ViGAT: Bottom-up event recognition and explanation in video using factorized graph attention network", IEEE Access, 2022.
My implementation of the original GAT paper (Veličković et al.). I've additionally included the playground.py file for visualizing the Cora dataset, GAT embeddings, an attention mechanism, and entropy histograms. I've supported both Cora (transductive) and PPI (inductive) examples!
The source codes for Fine-grained Fact Verification with Kernel Graph Attention Network.
This repository contain the unofficial implementation of the graph attention neural network.
Source code accompanying the paper "Reducing Over-smoothing in Graph Neural Networks Using Relational Embeddings" published in DLG-AAAI’23
异构图神经网络HAN。Heterogeneous Graph Attention Network (HAN) with pytorch
Bilateral Cross-Modality Graph Matching Attention for Feature Fusion in Visual Question Answering
Pytorch implementation of graph attention network
Gated-ViGAT. Code and data for our paper: N. Gkalelis, D. Daskalakis, V. Mezaris, "Gated-ViGAT: Efficient bottom-up event recognition and explanation using a new frame selection policy and gating mechanism", IEEE International Symposium on Multimedia (ISM), Naples, Italy, Dec. 2022.
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