Zero-permutation jet-parton assignment using a self-attention network. Preprint at https://doi.org/10.48550/arXiv.2012.03542 (2020). Larkoski, A. J., Moult, I. & Nachman, B. Jet substructure at the large Hadron collider: a review of recent advances in theory and machine learning. Phys....
本文首先介绍graph Embedding,为结构化的graph生成分布式表示;然后介绍graph convolutional network(图卷积),最后简单介绍基于图的序列建模。 【PDF版本已经发到github,需要自取 : talorwu/Graph-Neural-Network-Review】 【PPT版看这里】: Taylor Wu:Graph Neural Network Review(PPT)版200 赞同 · 9 评论文章 【其...
一个相关的应用是在设计新药物方面,希望找到具有特定属性的新分子图形,以作为治疗疾病的候选药物。 Reference A Gentle Introduction to Graph Neural Networks from Google Research Graph neural networks: A review of methods and applications 编辑于 2023-03-23 02:40・美国...
However, listing all graph network architectures would be beyond the scope of this review. Some of the earliest work on neural networks for molecular graphs dates back to the 90s and 2000s, without explicitly referring to the term graph neural network8,33. In 2017, a graph convolutional ...
2024, Neural Networks Show abstract Spatial–Temporal Complex Graph Convolution Network for Traffic Flow Prediction 2023, Engineering Applications of Artificial Intelligence Show abstract Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities 2022, Transportation Resea...
Graph pooling in graph neural networks: methods and their applications in omics studies 2024, Artificial Intelligence Review SpaNCMG: improving spatial domains identification of spatial transcriptomics using neighborhood-complementary mixed-view graph convolutional network 2024, Briefings in Bioinformatics Attentio...
such as graph neural networks, network embedding, representation learning, have led to unprecedented progress in solving many challenges facing real-world applications, such as recommender systems, anomaly detection, smart surveillance, traffic forecasting, disease control and prevention, medical diagnosis, ...
Graph Neural Network Library for PyTorch. Contribute to pyg-team/pytorch_geometric development by creating an account on GitHub.
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To address this problem, we propose a graph neural network-based bearing fault detection (GNNBFD) method. The method first constructs a graph using the similarity between samples; secondly the constructed graph is fed into a graph neural network (GNN) for feature mapping, and the samples output...