IEEE

GCN-RP: Graph Convolutional Network-Based Predictor for Network Connectivity Resilience

Abstract: Evaluating the connectivity resilience of real-world networks through attack simulations is a time-consuming process. This study proposes a method that trains Graph Convolutional Networks ...
Frontiers

DEG-BRIN-GCN: interpretable graph convolutional framework with differentially expressed genes brain region interaction network prior for AD diagnosis

Due to the intricate dynamic coupling between molecular networks and brain regions, early diagnosis and pathological mechanism analysis of Alzheimer's disease (AD) remain highly challenging. To ...
IEEE

Cross-Domain Explainable Recommendation Using Graph Convolutional Networks and a Topic Model

Abstract: Graph Convolutional Network (GCN)-based recommendation systems (RSs) have recently gained popularity for their ability to improve recommendation accuracy by utilizing neighborhood ...
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 ...
Medscape

In-Network Provider Use Rises Despite Bureaucratic Barriers

The percentage of patients receiving in-network care increased nationally by 7% across all medical specialties from 2019 to 2023, according to a new report. Overall, the national percentage of ...