Reinforced Spatiotemporal Attentive Graph Neural Networks for ...

Causality-Aware Spatiotemporal Graph Neural Networks for ...

Information systems → Data mining; Sensor networks. Keywords. Spatiotemporal Time Series Imputation, Spatiotemporal Graph Neu- ral Network, Causal Attention.

Context Integrated Relational Spatio-Temporal Resource Forecasting

To the best of our knowledge,. CIGNN is the first approach that integrates dynamic contextual information using graph neural networks for resource forecasting.

Rapid spatio-temporal flood modelling via hydraulics-based graph ...

In this paper, we introduce shallow water equation–graph neural network (SWE–GNN), a hydraulics-inspired surrogate model based on GNNs that can be used for ...

[D] Why I'm Lukewarm on Graph Neural Networks - Reddit

... reinforcement learning, and their connection to their discrete cousin (graph data). I like your point of view with regards to learning local ...

A review of graph neural networks: concepts, architectures ...

CNNs excel in processing grid-like data with spatial dependencies; GNNs are designed to handle graph-structured data with complex relationships ...

Graph autoencoder with mirror temporal convolutional networks for ...

The most advanced approach employs a graphical convolutional neural network (GNN) for spatial modeling reuse and combines LSTM to deal with ...

Graph Neural Networks and Reinforcement Learning: A Survey

Spatial graph convolutional networks and spectral graph convolutional networks are the two main branches of GCNs. The key idea in spectral GCN was defined by ...

Decoupled Dynamic Spatial-Temporal Graph Neural Network for ...

and also features a dynamic graph learning module that targets the learning of the dynamic characteristics of traffic networks. Exten- sive experiments with ...

GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural...

A generative pre-training framework for improving the spatio-temporal prediction performance of downstream models.

Graph Attention | Papers With Code

We present graph attention networks (GATs), novel neural network ... We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning ...

[AUTOML23]Understanding and Simplifying Architecture Search in ...

[AUTOML23]Understanding and Simplifying Architecture Search in Spatio-Temporal Graph Neural Networks. 301 views · 1 year ago ...more ...

Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic ...

Second, we design a novel graph neural network architecture, which can not only repre- sent dynamic spatial relevance among nodes with an improved multi-head ...

Graph Neural Networks for Traffic Prediction - page for sungsoo blog

Reinforced Spatio-Temporal Attentive Graph Neural Networks for Traffic Forecasting[J]. IEEE Internet of Things Journal, 2020. Link. Zhang W ...

Zero-Shot Video Object Segmentation via Attentive Graph Neural ...

First, our AGNN is unique in its spatial information preserving nature, which is opposed to conventional fully connected. GNNs and crucial for per-pixel ...

Understanding Graph Attention Networks: A Practical Exploration

Graph Attention Networks (GATs) are a variant of Graph Neural Networks (GNNs) that leverage attention mechanisms for feature learning on graphs.

STGATE: Spatial-temporal graph attention network with a ... - Frontiers

Electroencephalogram (EEG) is a crucial and widely utilized technique in neuroscience research. In this paper, we introduce a novel graph neural network ...

A Graph Neural Network with Spatio-temporal Attention ... - Hal-Inria

In particular, graph neural networks (GNN) and attention mechanisms were considered suitable for this problem, since they incorporate spatial ...

Adaptive Spatial–Temporal Aware Graph Learning for EEG-Based ...

The differences in neural activity between individuals are crucial for emotion recognition, but few models can effectively capture these interindividual ...

Spatial applications of Markov random fields and neural networks for ...

Thus, each chapter corresponds to a case study with applications in spatio-temporal denoising, causal inference, and reinforcement learning. Graph smoothing ...

Spatial-Temporal Video Representation for Content-based Retrieval

We propose a video feature representation learning framework called STAR-GNN, which applies a pluggable graph neural network component on a multi-scale ...