Results 311 to 320 of about 1,903,201 (339)

A graph neural network recommendation algorithm based on multi-scale attention and contrastive learning. [PDF]

Sci Rep
Pu D, Zhang Y, Qian Z, Xie G, Pu D.
europepmc   +1 more source

Enhanced Prediction of Absorption and Emission Wavelengths of Organic Compounds through Hybrid Graph Neural Network Architectures. [PDF]

ACS Omega
Nguyen DP, Le QM, Tran HTP, Le PT, Nguyen TVT.   +4 more
europepmc   +1 more source

Fault Diagnosis of Process Systems Based on Graph Neural Network

Wentao Ouyang, Yang Jin
openalex   +1 more source

SPX-GNN: An Explainable Graph Neural Network for Harnessing Long-Range Dependencies in Tuberculosis Classifications in Chest X-Ray Images. [PDF]

Diagnostics (Basel)
Pala MA, Navdar MB.
europepmc   +1 more source

Assessing zero-shot generalisation behaviour in graph-neural-network interatomic potentials.

Digit Discov
Ben Mahmoud C, El-Machachi Z, Gierczak KA, Gardner JLA, Deringer VL.   +4 more
europepmc   +1 more source

Graph in Graph Neural Network

arXiv.org
Existing Graph Neural Networks (GNNs) are limited to process graphs each of whose vertices is represented by a vector or a single value, limited their representing capability to describe complex objects.
Jiongshu Wang, Jing Yang, Jiankang Deng, Hatice Gunes, Siyang Song   +4 more
semanticscholar   +3 more sources

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DualGNN: Dual Graph Neural Network for Multimedia Recommendation

IEEE transactions on multimedia, 2023
One of the important factors affecting micro-video recommender systems is to model the multi-modal user preference on the micro-video. Despite the remarkable performance of prior arts, they are still limited by fusing the user preference derived from ...
Qifan Wang, Yin-wei Wei, Jianhua Yin, Jianlong Wu, Xuemeng Song, Liqiang Nie, Min Zhang   +6 more
semanticscholar   +1 more source

Graph Neural Networks

2021
Graphs are universal representations of pairwise relations with many real-world applications. In the healthcare domain, graphs are widely observed as relations of biomedical entities, including the graph structures of molecules, drug-drug interaction networks, protein-protein interaction networks, and gene expression networks and biomedical knowledge ...
Cao Xiao, Jimeng Sun
  +6 more sources

Traffic Flow Prediction via Spatial Temporal Graph Neural Network

The Web Conference, 2020
Traffic flow analysis, prediction and management are keystones for building smart cities in the new era. With the help of deep neural networks and big traffic data, we can better understand the latent patterns hidden in the complex transportation ...
Xiaoyang Wang, Yao Ma, Yiqi Wang, Wei Jin, Xin Wang, Jiliang Tang, Caiyan Jia, Jian Yu   +7 more
semanticscholar   +1 more source

Graph Neural Network for Fraud Detection via Spatial-Temporal Attention

IEEE Transactions on Knowledge and Data Engineering, 2022
Card fraud is an important issue and incurs a considerable cost for both cardholders and issuing banks. Contemporary methods apply machine learning-based approaches to detect fraudulent behavior from transaction records.
Dawei Cheng, Xiaoyang Wang, Ying Zhang, Liqing Zhang   +3 more
semanticscholar   +1 more source