A Novel Spatio-Temporal Generative Inference Network for Predicting the Long-Term Highway Traffic Speed
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Updated
Jul 2, 2024 - Python
A Novel Spatio-Temporal Generative Inference Network for Predicting the Long-Term Highway Traffic Speed
This project is a scalable unified framework for deep graph clustering.
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
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
This repository is a brief tutorial about how Graph convolutional networks and message passing networks work with example code demonstration using pytorch and torch_geometric
[ICDE2023] A PyTorch implementation of Self-supervised Trajectory Representation Learning with Temporal Regularities and Travel Semantics Framework (START).
Using to predict the highway traffic speed
[ICDE'2023] When Spatio-Temporal Meet Wavelets: Disentangled Traffic Forecasting via Efficient Spectral Graph Attention Networks
Differentiable clustering for graph attention-TKDE 2024
PyTorch implementation of MTAD-GAT (Multivariate Time-Series Anomaly Detection via Graph Attention Networks) by Zhao et. al (2020, https://arxiv.org/abs/2009.02040).
An Explainable Geometric-Weighted Graph Attention Network (xGW-GAT) for Identifying Functional Networks Associated with Gait Impairment
learning station embedding
Official implementation for "Tailoring Self-Attention for Graph via Rooted Subtrees" (NeurIPS2023)
Official implementation for "Tailoring Self-Attention for Graph via Rooted Subtrees" (NeurIPS2023)
PyTorch implementation of the Graph Attention Networks (GAT) based on the paper "Graph Attention Network" by Velickovic et al - https://arxiv.org/abs/1710.10903v3
Source code for paper "Conversational Question Answering over Knowledge Graphs with Transformer and Graph Attention Networks"
When Will We Arrive? A Novel Multi-Task Spatio-Temporal Attention Network Based on Individual Preference for Estimating Travel Time
Pytorch implementation of the Graph Attention Network model by Veličković et. al (2017, https://arxiv.org/abs/1710.10903)
[TSAS 2023] AIST: An Interpretable Attention-based Deep Learning Model for Crime Prediction.
Flora is an new GNN-based Open Source tool can apply to Dreamplace post-processing, so that can achieve faster and more accurate layout design.
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