Reinforced Spatio|Temporal Attentive Graph Neural Networks for ...

Temporal Graph Neural Networks for Social Recommendation

relations (user-item, user-user, item-item), we propose a novel attentive cross-view training strategy to aggregate the node information in different views. ‚ ...

Recurrent Space-time Graph Neural Networks - NIPS papers

Their method is applied on program evaluation, simulated environments used in reinforcement learning and language modeling where they do not have a spatial ...

Attentive graph structure learning embedded in deep spatial ...

As a result, for efficient graph construction, a Deep Spatial-Temporal Graph Neural Network (DSTGNN) is proposed for the construction of the weighted adjacency ...

Spatial-temporal graph neural ODE networks for skeleton-based ...

We propose an innovative model called spatial-temporal graph neural ordinary differential equations (STG-NODE).

Deep reinforcement learning guided graph neural networks for brain ...

2024: Attentive graph structure learning embedded in deep spatial-temporal graph neural network for traffic forecasting Applied Intelligence 54 · PreviousNext ...

Spatial-temporal graph convolution network model with traffic ... - OUCI

Wang, Traffic Flow Prediction via Spatial Temporal Graph Neural Network, с. ... Zhou, Reinforced Spatio-Temporal Attentive Graph Neural Networks for Traffic ...

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 ...

Traffic Speed Forecasting Via Spatio-Temporal Attentive Graph ...

Reinforced Spatiotemporal Attentive Graph Neural Networks for Traffic Forecasting. Fan Zhou, Q. Yang, Kunpeng Zhang, Goce Trajcevski, Ting Zhong, A. Khokhar.

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 ...

Search for Spatial Temporal Graph | Papers With Code

We propose a neural network-based Spatial-Temporal Interactive Dynamic Graph Convolutional Network (STIDGCN) to address the above challenges for traffic ...

Graph Neural Networks for temporal graphs: State of the art, open ...

Abstract. Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static) graph-structured data. However, many.

EEG Based Emotion Analysis Using Reinforced Spatio-Temporal ...

After that, the EEG signal emotions are classified using Reinforced Spatio-Temporal Attentive Graph Neural Networks method. RSTAGNN weight parameters are ...

Forecasting infections with spatio-temporal graph neural networks

How to stop epidemics: controlling graph dynamics with reinforcement learning and graph neural networks (2020). ... How attentive are graph attention networks? ( ...

Graph construction on complex spatiotemporal data for enhancing ...

Graph neural networks (GNNs) haven proven to be an indispensable approach in modeling complex data, in particular spatial temporal data, ...

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 ...

ICML 2024 Papers

How Interpretable Are Interpretable Graph Neural Networks? Doubly Robust ... High-Performance Temporal Reversible Spiking Neural Networks with ...

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 ...

Scalable Spatiotemporal Graph Neural Networks

Graph neural networks (GNNs) are often the core component of the forecasting architecture. However, in most spatiotemporal GNNs, the computational complexity.

"Graph Neural Networks and Applications to Deep Reinforcement ...

Center for the Fundamental Physics of the Universe (CFPU) Student Machine Learning Initiative (SMLI) - Recorded December 1, ...

Master's in Artificial Intelligence | Computer & Data Science Online

... learning methods, especially those based on temporal difference learning ... attentive neural networks, and pre-training / transfer learning. What You ...