Results 121 to 130 of about 8,038,825 (297)
GTAT: empowering graph neural networks with cross attention
Scientific ReportsGraph Neural Networks (GNNs) serve as a powerful framework for representation learning on graph-structured data, capturing the information of nodes by recursively aggregating and transforming the neighboring nodes’ representations.
Jiahao Shen +5 more
doaj +1 more source
Survey of Knowledge Graph Representation Learning for Relation Feature Modeling [PDF]
Jisuanji kexueKnowledge graph representation learning techniques can transform symbolic knowledge graphs into numerical representations of entities and relations,and then effectively combine various deep learning models to facilitate downstream applications of ...
NIU Guanglin, LIN Zhen
doaj +1 more source
A Lightweight Procedural Layer for Hybrid Experimental–Computational Workflows in Materials Science
Advanced Engineering Materials, EarlyView.We unveil a prototype hybrid‐workflow framework that fuses automatedcomputation with hands‐on experiments. Built atop pyiron, a lightweight, parameterized layer translates procedure descriptions into executable manual steps, syncing instrument settings, human interventions, and data capture in real‐time today.
Steffen Brinckmann +8 more
wiley +1 more source
Explore contrastive learning on graph representation learning
, 2022 Graph is a type of structured data to describe the multiple objects as well as their relationships, and is attracting increasing attention in recent years as it can represent various types of data structures in the real life, such as the social networks,
Liu, Qiuyu
core
Representation Learning on Graphs: Methods and Applications
IEEE Data Eng. Bull., 2017 Published in the IEEE Data Engineering Bulletin, September 2017; version with minor ...
William L. Hamilton +2 more
openaire +4 more sources
PASTA‐ELN: Simplifying Research Data Management for Experimental Materials Science
Advanced Engineering Materials, EarlyView.Research data management faces ongoing hurdles as many ELNs remain complex and restrictive. PASTA‐ELN offers an open‐source, cross‐platform solution that prioritizes simplicity, offline access, and user control. Its in tuitive folder structure, modular Python add‐ons, and open formats enable seamless documentation, FAIR data practices, and easy ...
S. Brinckmann, G. Winkens, R. Schwaiger
wiley +1 more source
Molecular subgraph representation learning based on spatial structure transformer
Complex & Intelligent SystemsIn the field of molecular biology, graph representation learning is crucial for molecular structure analysis. However, challenges arise in recognising functional groups and distinguishing isomers due to a lack of spatial structure information. To address
Shaoguang Zhang +2 more
doaj +1 more source
Advanced Engineering Materials, EarlyView.A novel workflow for investigating hydride vapor phase epitaxy for GaN bulk crystal growth is proposed. It combines Design of experiments (DoE) with physical simulations of mass transport and crystal growth kinetics, serving as an intermediate step between DoE and experiments.
J. Tomkovič +7 more
wiley +1 more source
Advanced Engineering Materials, EarlyView.Reproduction of stacking fault energy calculations from literature with a semi‐automated large language model‐assisted extraction procedure: extraction of simulation protocol, atomistic structures, computational parameters, and reported results, ontology alignment, knowledge graph construction and, finally, recomputation forvalidation.
Sepideh Baghaee Ravari +5 more
wiley +1 more source
Variational Graph Convolutional Networks for Dynamic Graph Representation Learning
IEEE AccessThe ubiquitous and ever-evolving nature of cyber threats demands innovative approaches that can adapt to the dynamic relationships and structures within network data.
Aabid A. Mir +4 more
doaj +1 more source