Results 91 to 100 of about 537,310 (265)

Large-scale knowledge graph representation learning

Knowledge and Information Systems
Abstract The knowledge graph emerges as powerful data structures that provide a deep representation and understanding of the knowledge presented in networks. In the pursuit of representation learning of the knowledge graph, entities and relationships undergo an embedding process, where they are mapped onto a vector space with reduced dimensions.
Badrouni, Marwa, Katar, Chaker, Inoubli, Wissem   +2 more
openaire   +2 more sources

Deep Learning for Learning Graph Representations

, 2019
Mining graph data has become a popular research topic in computer science and has been widely studied in both academia and industry given the increasing amount of network data in the recent years. However, the huge amount of network data has posed great challenges for efficient analysis.
Zhu, Wenwu, Wang, Xin, Cui, Peng
openaire   +2 more sources

Amyloidogenic Peptide Fragments Designed From Bacterial Collagen‐like Proteins Form Hydrogel

Advanced Functional Materials, EarlyView.
This study identified amyloidogenic sequence motifs in bacterial collagen‐like proteins and exploited these to design peptides that self‐assemble into β‐sheet fibers and form hydrogels. One hydrogel supported healthy fibroblast growth, showing promise for biocompatible materials. Our work demonstrates that bacterial sequences can be harnessed to create
Vamika Sagar, Xuen J Ng, Lukas Lechel, Ashish Chand Rajwar, Hendrik Bargel, Abhishek A. Jalan   +5 more
wiley   +1 more source

GCL-ALG: graph contrastive learning with adaptive learnable view generators [PDF]

PeerJ Computer Science
Data augmentation is a pivotal part of graph contrastive learning, which can mine implicit graph data information to improve the quality of representation learning.
Yafang Li, Jie Kang, Zhihua Chu, Baokai Zu   +3 more
doaj   +2 more sources

Engineering Strategies for Stable and Long‐Life Alkaline Zinc‐Based Flow Batteries

Advanced Functional Materials, EarlyView.
Alkaline zinc‐based flow batteries face persistent challenges from unstable zinc deposition, including dendrite growth, passivation, corrosion, and hydrogen evolution, which severely limit cycling stability. Current research addresses these issues through coordinated electrode structuring, electrolyte regulation, and membrane design to control zinc ...
Yuran Bai, Xiaoyu Huo, Xin Long, Enkang Fu, Xusong Gong, Lei Wei, Liang An   +6 more
wiley   +1 more source

Graph latent diffusion-based molecular representation learning for enhanced generalization in molecular property prediction

Journal of Cheminformatics
This study aims to evaluate the effect of latent diffusion models on molecular representation learning from the perspective of generalization performance in molecular property prediction.
Daiki Koge, Naoaki Ono, Takashi Abe, Shigehiko Kanaya   +3 more
doaj   +1 more source

Active Learning‐Accelerated Discovery of Fibrous Hydrogels with Tissue‐Mimetic Viscoelasticity

Advanced Functional Materials, EarlyView.
Active learning accelerates the design of fibrous hydrogels that mimic the viscoelasticity of native tissues. By integrating multi‐objective optimization and closed‐loop experimentation, this approach efficiently identifies optimal formulations from thousands of possibilities and decouples elasticity and viscosity. The resulting hydrogels offer tunable
Zhengkun Chen, Zheyuan Miao, Gary Tom, Austin H. Cheng, Elizaveta Gusarova, Benjamen Zondag, Tianyi Wu, Dianoosh Kalhori, Boris Hinz, Milad Abolhasani, Alán Aspuru‐Guzik, Eugenia Kumacheva   +11 more
wiley   +1 more source

GTAT: empowering graph neural networks with cross attention

Scientific Reports
Graph 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, Qura Tul Ain, Yaohua Liu, Banqing Liang, Xiaoli Qiang, Zheng Kou   +5 more
doaj   +1 more source

Self‐Healing and Stretchable Synaptic Transistor

Advanced Functional Materials, EarlyView.
A self‐healing stretchable synaptic transistor (3S‐T) is realized using a p‐PVDF‐HFP‐DBP/PDMS‐MPU‐IU bilayer as gate insulator, where dipole‐dipole interaction enhances polarization to achieve a large memory window. Leveraging its neuronal biomimicry, the synaptic transistor demonstrates electrically compatibility with the biological brain. Furthermore,
Hyongsuk Choo, Sangyong Park, Yoonyi Jeong, Jaepyo Jang, Duhwan Seong, In Soo Kim, Myung‐Gil Kim, Mikyung Shin, Minah Suh, Donghee Son, Jin‐Hong Park   +10 more
wiley   +1 more source

Survey of Knowledge Graph Representation Learning for Relation Feature Modeling [PDF]

Jisuanji kexue
Knowledge 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