GraphPro is a versatile and pluggable OO python library designed for leveraging deep graph learning representations to gain insights into structural proteins and ...

Abstract: Deep graph convolutional networks (GCNs) have shown promising performance in traffic prediction tasks, but their practical deployment on resource-constrained devices faces challenges. First, ...

Then we propose a Decoupled Adaptive Graph Convolution Attention Network for Traffic Forecasting (DAGCAN), which relies on the above two modules to dynamically capture the fine-grained spatio-temporal ...

This repo contains an example implementation of the Simple Graph Convolution (SGC) model, described in the ICML2019 paper Simplifying Graph Convolutional Networks. SGC removes the nonlinearities and ...

Methods: To address these challenges, we propose PoseGCN, a Graph Convolutional Network (GCN)-based model that integrates spatial, temporal, and contextual features through a novel framework. PoseGCN ...

Disclaimer: AlphaGalileo is not responsible for the accuracy of news releases posted to AlphaGalileo by contributing ...

In 2031, it will range between $1.68 and $1.82, with an average price of $1.75. The Graph offers access to competitive and cost-efficient decentralized data sets. The network boasts a 99.99% uptime ...

We propose a deep-learning framework using convolutional neural networks (CNNs) to predict the quantitative traits from SNPs and also to investigate genotype contributions to the trait using saliency ...