Differentiable clustering for graph attention-TKDE 2024
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Updated
Mar 5, 2024 - Python
Differentiable clustering for graph attention-TKDE 2024
This is the official repo of paper: Multi-modal Protein-Drug Interaction Prediction Via Attention-based Network. Code and pretrained model weights are available here.
This project aims to implement the dependency-graph-convolutional-networks and dependency-graph-attention-networks for span detection from given text.
Official implementation for "Tailoring Self-Attention for Graph via Rooted Subtrees" (NeurIPS2023)
Implementation of Relational Graph Attention operator for heterogeneous graphs in PyTorch
learning station embedding
Molecular substructure graph attention network for molecular property identification in drug discovery. This is the starting point for my thesis project and is the fork of a repository from the paper https://doi.org/10.1016/j.patcog.2022.108659
[NIPS 2020] Graph Geometry Interaction Learning
Graph Attention Networks (GATs)
This project aims to develop dependency-graph-attention-networks in order to represent the dependency relations of each word from given text utilizing masked self-attention. The output of the dependency-graph-attention-networks is the token-level representation of the sum of the token and its dependency.
Pytorch implementation of the Graph Attention Network with visualizations
Code for the paper "NABU - Multilingual Graph-based Neural RDF Verbalizer"
Implementation of CAGNIR, a new Neural Information Retrieval model aggregates relevant semantics through applying Graph Attention Networks on the Click Graph.
Source code for my honours thesis: "Graph Attention Networks for Compositional Visual Question Answering"
PyTorch implementation of the paper "Graph Attention Networks". (ICLR 2018)
Master thesis: JAT (Jraph Attention Networks), a deep learning architecture to predict the potential energy and forces of molecules. Adapts Graph Attention Networks (GATv2) within the Message Passing Neural Networks framework to computational chemistry in JAX
A convenient wrapper to develop graph neural networks with Keras. Currently under development with the objective of integrating Networkx, Owlready2 and oneM2M for cognitive IoT.
Using Graph Attention NN for image embedding and classification
Heterogeneous Graph Attention Networks for Early Detection of Rumors on Twitter (IJCNN 2020)
[TSAS 2023] AIST: An Interpretable Attention-based Deep Learning Model for Crime Prediction.
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