Heterogeneous Graph Neural Network
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Updated
May 6, 2020 - Python
Heterogeneous Graph Neural Network
The source codes for Fine-grained Fact Verification with Kernel Graph Attention Network.
异构图神经网络HAN。Heterogeneous Graph Attention Network (HAN) with pytorch
The GitHub repository for the paper "Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations"
PyTorch implementation of Graph Attention Networks
Course project of SJTU CS3319: Foundations of Data Science, 2023 spring
Bilateral Cross-Modality Graph Matching Attention for Feature Fusion in Visual Question Answering
Pytorch implementation of graph attention network
A deep learning library to rank protein complexes using graph neural networks
Modeling Extent-of-Texture Information for Ground Terrain Recognition
Source code accompanying the paper "Reducing Over-smoothing in Graph Neural Networks Using Relational Embeddings" published in DLG-AAAI’23
Graph Attention Networks for Entity Summarization is the model that applies deep learning on graphs and ensemble learning on entity summarization tasks.
Keyphrase extraction using graph convolution
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.
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.
A Drug Metabolite & Toxicity Property Predictor Based on Graph Neural Network
Dense and Sparse Implementation of GAT written by PyTorch
An implementation of multi-view graph attention network
Graph Attention Networks (GATs)
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