Results 81 to 90 of about 35,453 (267)

Multi-Task Prediction Method Based on GGCN for Object Centric Event Logs

IEEE Access
Event logs constitute the fundamental data for predictive process monitoring research, and the quality and format of these logs are crucial for predictive analysis.
Li Ke, Fang Huan, Xu Yifei, Shao Chifeng
doaj   +1 more source

Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High‐Performance Cu Alloys

Advanced Science, EarlyView.
By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin, Qian Lei, Wei Yan, Yue Li, Tong Xie, Shuang Zhou, Chong Wang, Bram Hoex, Qiang Long, Caoyang Jiang, Min Song, Zhou Li, Zhangwei Wang   +12 more
wiley   +1 more source

Bayesian graph convolutional network with partial observations.

PLoS ONE
As a widely studied model in the machine learning and data processing society, graph convolutional network reveals its advantage in non-grid data processing.
Shuhui Luo, Peilan Liu, Xulun Ye
doaj   +1 more source

Graph Neural Networks: A Bibliometric Mapping of the Research Landscape and Applications

Information
Graph neural networks (GNNs) are deep learning algorithms that process graph-structured data and are suitable for applications such as social networks, physical models, financial markets, and molecular predictions.
Annielle Mendes Brito da Silva, Natiele Carla da Silva Ferreira, Luiza Amara Maciel Braga, Fabio Batista Mota, Victor Maricato, Luiz Anastacio Alves   +5 more
doaj   +1 more source

Efficient Screening of Organic Singlet Fission Molecules Using Graph Neural Networks

Advanced Science, EarlyView.
A high‐throughput screening framework based on graph neural networks (GNNs) and multi‐level validation facilitates the identification of singlet fission (SF) candidates. By efficiently predicting excitation energies across 20 million molecules, and integrating TDDFT calculations, synthetic accessibility assessments, and GW+BSE calculations, this ...
Li Fu, Longfei Lv, Fan Zhang, Si Zhou, Weiwei Gao, Jijun Zhao   +5 more
wiley   +1 more source

Simple and Deep Graph Convolutional Networks

CoRR, 2020
Graph convolutional networks (GCNs) are a powerful deep learning approach for graph-structured data. Recently, GCNs and subsequent variants have shown superior performance in various application areas on real-world datasets. Despite their success, most of the current GCN models are shallow, due to the {\em over-smoothing} problem.
Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, Yaliang Li   +4 more
openaire   +3 more sources

Graph Convolutional Recommendation System Based on Bilateral Attention Mechanism

Journal of Engineering
Collaborative Filtering Recommender Systems face data sparsity and cold-start issues, leading to a decrease in their recommendation performance. Therefore, numerous researchers have integrated knowledge graphs and graph convolutional networks into ...
Hui Yang, Changchun Yang
doaj   +1 more source

SMS spam detection using BERT and multi-graph convolutional networks

International Journal of Intelligent Networks
The surge in smartphone usage has significantly increased Short Message Service (SMS) traffic and, consequently, SMS spam, posing risks such as phishing, financial losses, and privacy breaches.
Linjie Shen, Yanbin Wang, Zhao Li, Wenrui Ma   +3 more
doaj   +1 more source

CMOS‐Integrated Synaptic Photoreceptor Chip Inspired by Insect Visual Processing

Advanced Science, EarlyView.
CMOS‐integrated Si QDs/ReS2 synaptic photoreceptor array mimics the parallel processing and wavelength‐selective strategy of insect vision. By combining intrinsic ultraviolet‐violet sensitivity with synaptic plasticity, the chip enables frontend sensory redundancy reduction without external filters, offering a scalable pathway toward lowpower ...
Jian Chai, Xinyi Xu, Yue Wang, Xiaochen Wang, Hailiang Wang, Yunfei Xie, Xinwei Zhang, Haoyu Wang, Hao Ning, Jiangming Lin, Yongliang Xie, Qihai Jiang, Baoshi Qiao, Xiaolei Ding, Luyao Ma, Shukai Duan, Zhenyi Ni, Huan Hu, Xin He, Fei Xue, Feichi Zhou, Lingfei Li, Srikrishna Chanakya Bodepudi, Rui Yuan, Bin Yu, Yang Xu   +25 more
wiley   +1 more source

From Spectral Graph Convolutions to Large Scale Graph Convolutional Networks

CoRR, 2022
Graph Convolutional Networks (GCNs) have been shown to be a powerful concept that has been successfully applied to a large variety of tasks across many domains over the past years. In this work we study the theory that paved the way to the definition of GCN, including related parts of classical graph theory.
openaire   +2 more sources