Tech Xplore

New graph attention network models higher-order relationships in complex graph data

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...
GitHub

Multimodal Emotion Recognition in Conversations via Graph Structure Learning

Python 3.10.13 PyTorch 1.13.0 torch_geometric 2.5.2 torch-cluster 1.6.1 torch-scatter 2.1.1 torch-sparse 0.6.17 torch-spline-conv 1.2.2 sparsemax 0.1.9 CUDA 11.7 Train RIGSL using the MELD dataset.
IEEE

Tracing Intricate Cues in Dialogue: Joint Graph Structure and Sentiment Dynamics for Multimodal Emotion Recognition

Abstract: Multimodal emotion recognition in conversation (MERC) has garnered substantial research attention recently. Existing MERC methods face several challenges: (1) they fail to fully harness ...
InfoQ

Virtual Panel - AI in the Trenches: How Developers Are Rewriting the Software Process

This virtual panel brings together engineers, architects, and technical leaders to explore how AI is changing the landscape ...
GitHub

UrbanGraph: Physics-Informed Spatio-Temporal Dynamic Heterogeneous Graphs for Urban Microclimate Prediction

This repository contains the official PyTorch implementation and the UMC4/12 Dataset for the paper: [UrbanGraph: Physics-Informed Spatio-Temporal Dynamic Heterogeneous Graphs for Urban Microclimate ...
IEEE

AGSEI: Adaptive Graph Structure Estimation With Long-Tail Distributed Implicit Graphs

Abstract: Empowered by their remarkable advantages, graph neural networks (GNN) serve as potent tools for embedding graph-structured data and finding applications across various domains. Particularly, ...