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PolyU develops novel AI graph neural network models to unravel interdisciplinary complexities in image recognition and neuroscience

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models ...
Psychology Today

Why the New Artificial Intelligence Is So Powerful

AI became powerful because of interacting mechanisms: neural networks, backpropagation and reinforcement learning, attention, ...
IEEE

Wasserstein Graph Neural Networks for Graphs With Missing Attributes

Abstract: Missing node attributes pose a common problem in real-world graphs, impacting the performance of graph neural networks’ representation learning. Existing GNNs often struggle to effectively ...
IEEE

Dynamical Causal Graph Neural Network for EEG Emotion Recognition

Abstract: Recently, topological graphs based on structural or functional connectivity of brain network have been utilized to construct graph neural networks (GNN) for Electroencephalogram (EEG) ...
Microsoft

Heterogeneous Graph Neural Network on Semantic Tree

The recent past has seen an increasing interest in Heterogeneous Graph Neural Networks (HGNNs), since many real-world graphs are heterogeneous in nature, from citation graphs to email graphs. However, ...
University of Wyoming

UW and Wyoming SBDC Network to Host Patent Introduction Webinar Nov. 12

Small-business owners, entrepreneurs and startups will have the opportunity to receive an introduction to patents Wednesday, Nov. 12. Mary Fuller, U.S. Patent and Trademark Office (USPTO) regional ...
Finextra

Graph Neural Networks and GraphRAG: Navigating Open-World Complexity in Finance

Graph Neural Networks (GNNs) and GraphRAG don’t “reason”—they navigate complex, open-world financial graphs with traceable, multi-hop evidence. Here’s why BFSI leaders should embrace graph-native AI ...
Frontiers

Complex-valued brain networks for neurodegenerative disease diagnosis via component-aware feature fusion

Introduction: Recent advancements in brain network analysis have greatly improved the diagnosis of neurodegenerative diseases. However, most existing studies rely on single-frequency EEG ...
C&EN

GGCRB: A Graph Neural Network Approach for Predicting CircRNA–RBP Interactions Using Structural and Sequence Features

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. The interaction between circular RNAs (circRNAs) and RNA-binding proteins (RBPs) plays ...
Frontiers

DGAT: a dynamic graph attention neural network framework for EEG emotion recognition

Introduction: Emotion recognition based on electroencephalogram (EEG) signals has shown increasing application potential in fields such as brain-computer interfaces and affective computing. However, ...
Hackaday

A Neural Net For A Graphing Calculator?

Machine learning and neural nets can be pretty handy, and people continue to push the envelope of what they can do both in high end server farms as well as slower systems. At the extreme end of the ...