AI-powered knowledge graph links heart images to genes and drug predictions

Knowledge graphs are a powerful tool for bringing together information from biological databases and linking what is already ...
EurekAlert!

Accelerated streaming subgraph matching framework is faster, more robust, and scalable

Graphs are widely used to represent complex relationships in everyday applications such as social networks, bioinformatics, ...
Frontiers

A multi-graph convolutional network method for Alzheimer’s disease diagnosis based on multi-frequency EEG data with dual-mode connectivity

Objective: Alzheimer’s disease (AD) is mainly identified by cognitive function deterioration. Diagnosing AD at early stages poses significant challenges for both researchers and healthcare ...
IEEE

A Nonlinear Weighted Graph Convolution Network Based on Manifold Geometric Regularization for Hyperspectral Image Classification

Abstract: Extracting spatial–spectral joint features has become a critical approach for improving model classification performance in the field of hyperspectral image classification (HSIC). However, ...
Search Engine Land

Google’s great clarity cleanup: 3 shifts redefining the Knowledge Graph and its AI future

Google’s Knowledge Graph saw its largest contraction in a decade in June: a two-stage, one-week drop of 6.26% – over 3 billion entities deleted. Since 2015, we’ve tracked the Knowledge Graph and have ...
IEEE

Augmented Graph Convolutional Network for Enhancing Label Reachability

Abstract: Graph Convolutional Networks (GCNs) have emerged as a leading approach for semi-supervised node classification. However, due to the uneven distribution of labeled nodes in graphs, only a ...