生物通

AI增强计算机辅助设计：建模操作的预测建模

该研究提出一种基于图的CAD辅助方法，可在参数化设计序列中预测下一个建模操作。研究人员将来自汽车领域的真实CATIA V5模型转换为有向无环图（Directed Acyclic Graph，DAG）以捕获特征依赖关系，从而实现直接从结构设计数据中学习。所采用的 该研究提出一种基于图的CAD辅助方法，可在参数化设计序列中预测下一个建模操作。研究人员将来自汽车领域的真实CATIA V5模型转换为有向无 ...
生物通

中国碳达峰约束下不同社会经济发展路径(Shared Socioeconomic Pathways, SSPs ...

摘要：2030年是中国落实联合国《2030年可持续发展议程》与履行《巴黎协定》碳达峰承诺的交汇节点。在资源与时间受限条件下，协调脱碳目标与可持续发展目标(Sustainable Development Goals, SDGs)是核心治理难题。本研究提出一个融合 摘要：2030年是中国落实联合国《2030年可持续发展议程》与履行《巴黎协定》碳达峰承诺的交汇节点。在资源与时间受限条件下，协调脱碳目标与 ...
AlphaGalileo

AdapGNN: A New Framework Enhances Explainability of Graph Neural Networks for Molecular ...

Researchers have developed AdapGNN, a novel model-agnostic framework that addresses the oversmoothing problem in graph neural ...
AlphaGalileo

Crossing Virtual Boundaries: Rethinking Social Network Alignment via Graph Learning

Sub-headline: HUST researchers systematize SNA methods, building an evolutionary taxonomy based on graph representation ...
IEEE

Modularized Graph Convolutional Network

Abstract: Dear Editor, This letter presents a novel graph neural network, namely modularized graph convolution network (MGCN), to address the underexplored issue in graph convolution networks (GCNs), ...
IEEE

Graph Tensor Convolutional Network

Abstract: A dynamic graph (DG) is commonly encountered in many big data-related application scenarios, like cryptocurrency transaction analysis. A dynamic graph convolutional network (GCN) can ...
The Lancet

Using graph convolutional network to characterize individuals with major depressive ...

eDepartment of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK fDepartment of Psychosis Studies, Institute of Psychiatry, Psychology ...
Frontiers

Dual-channel deep graph convolutional neural networks

The dual-channel graph convolutional neural networks based on hybrid features jointly model the different features of networks, so that the features can learn each other and improve the performance of ...
GitHub

Semantic-STGCNN: A Semantic Spatio-Temporal Graph Convolutional Neural Network for Human ...

Semantic-STGCNN is a novel deep learning framework for human trajectory prediction that integrates semantic environmental information with spatio-temporal graph convolutional neural networks. This ...