Results 121 to 130 of about 145,055 (276)

Inverse link prediction with graph convolutional networks for knowledge-preserving sparsification in cheminformatics

Journal of Big Data
Large-scale cheminformatics datasets, such as those used in drug discovery and materials science, are often represented as dense similarity graphs; however, their complexity hinders scalable analysis and interpretability.
Elnaz Bangian Tabrizi, Mehrdad Jalali, Mahboobeh Houshmand   +2 more
doaj   +1 more source

Variational Graph Convolutional Networks for Dynamic Graph Representation Learning

IEEE Access
The 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, Megat F. Zuhairi, Shahrulniza Musa, Meshari H. Alanazi, Abdallah Namoun   +4 more
doaj   +1 more source

Review of Blockchain Application With Graph Neural Networks, Graph Convolutional Networks and Convolutional Neural Networks

This paper reviews the applications of Graph Neural Networks (GNNs), Graph Convolutional Networks (GCNs), and Convolutional Neural Networks (CNNs) in blockchain technology. As the complexity and adoption of blockchain networks continue to grow, traditional analytical methods are proving inadequate in capturing the intricate relationships and dynamic ...
Amy Ancelotti, Claudia Liason
openaire   +2 more sources

Memristive Physical Reservoir Computing

Advanced Science, EarlyView.
Memristors’ nonlinear dynamics and input‐dependent memory effects make them ideal candidates for high‐performance physical reservoir computing (RC). Based on their conductance modulation, memristors can be classified as electronic or optoelectronic types.
Dian Jiao, Ziyuan Wang, Jingrui Wang, Li Zhang, Xianhua Wei, Lingxiang Hu, Zongxiao Li, Athanasios V. Vasilakos, Zhizhen Ye, Fei Zhuge   +9 more
wiley   +1 more source

Rotation Invariance in Graph Convolutional Networks [PDF]

Annals of computer science and information systems, 2021
Nguyen Anh Mac, Hung Son Nguyen
doaj   +1 more source

SpaMode: A Broadly Applicable Framework for Deciphering Spatial Multi‐Omics Using Multimodal Mixture of Disentangled Experts

Advanced Science, EarlyView.
SpaMode introduces a versatile framework for spatial multi‐omics integration across vertical, horizontal, and mosaic scenarios. By disentangling modality‐invariant and variant features through a mixture‐of‐experts mechanism, it adaptively reconfigures spatially heterogeneous signals.
Xubin Zheng, Xinlei Huang, Xiang Zhou, Dian Meng, Ziyue Qiao, Zhiyuan Yuan, Lixin Cheng   +6 more
wiley   +1 more source

A review on the applications of graph neural networks in materials science at the atomic scale

Materials Genome Engineering Advances
In recent years, interdisciplinary research has become increasingly popular within the scientific community. The fields of materials science and chemistry have also gradually begun to apply the machine learning technology developed by scientists from ...
Xingyue Shi, Linming Zhou, Yuhui Huang, Yongjun Wu, Zijian Hong   +4 more
doaj   +1 more source

Combining Spatial Multi‐Omics Data to Decipher Spatial Domains and Elucidate Cell Heterogeneity Based on Self‐Supervised Graph Learning

Advanced Science, EarlyView.
A self‐supervised multi‐view graph fusion framework integrates spatial multi‐omics, excelling in domain identification and denoising. It reconstructs spatial pseudo‐expression, jointly analyzes multi‐omics data, infers RNA velocity, predicts spatial omics features from single‐cell multi‐omics, and detects spatially dark genes and transcription factors,
Yuejing Lu, Rui Qiao, Ying Li, Junhong Li, Nuoya Yue, Jing Ge, Jiao Wang, Luonan Chen, Peiluan Li   +8 more
wiley   +1 more source

A Portable and Dual‐Button Microneedle Device Enables Intelligent Multimodal Laser Sensing

Advanced Science, EarlyView.
A portable and dual‐button microneedle device enables rapid interstitial fluid sampling. Coupled with multimodal laser sensing and AI‐assisted data processing, the platform enables simultaneous molecular and elemental analysis for minimally invasive and multiplexed health assessment toward point‐of‐care diagnostics.
Yuanchao Liu, Xiujuan Hu, Shengqun Shi, Bowen Li, Zhixing Ge, Yunchen Long, Chaochao Sun, Annan Chen, Bingbing Gao, Lianbo Guo, Condon Lau, Wei Luo, Chwee Teck Lim   +12 more
wiley   +1 more source

Graph Convolutional Neural Network [PDF]

Procedings of the British Machine Vision Conference 2016, 2016
Michael Edwards, Xianghua Xie
openaire   +2 more sources