(5) layer connection.GNNs mainly has five branches,Graph Convolution Neural Network(GCNN), Graph ...
The quantum graph neural networks have many possibilities as applications from the simulation perspective of quantum dynamics. Among the application models of various quantum graph neural networks, the quantum graph recurrent neural network (QGRNN) is proven to be effective in training the Ising model...
In drug design, compound potency prediction is a popular machine learning application. Graph neural networks (GNNs) predict ligand affinity from graph representations of protein–ligand interactions typically extracted from X-ray structures. Despite some
In addition to the easy application of existing GNNs, PyG makes it simple to implement custom Graph Neural Networks (see here for the accompanying tutorial). For example, this is all it takes to implement the edge convolutional layer from Wang et al.: x i ′ = max j ∈ N ( i ) MLP...
Convolutional Neural Networks on Graphs (by Xavier Bresson): 以Spectral的方法为主，介绍了这个方向的...
Graph neural networks (GNNs) have received intense interest as a rapidly expanding class of machine learning models remarkably well-suited for materials applications. To date, a number of successful GNNs have been proposed and demonstrated for systems ra
Wang, M. et al. Deep Graph Library: a graph-centric, highly-performant package for graph neural networks. Preprint at https://arxiv.org/abs/1909.01315 (2019). Alidaee, B., Kochenberger, G. A. & Ahmadian, A. 0-1 Quadratic programming approach for optimum solutions of two scheduling pro...
This repository contains the source code for the paper scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses. Juexin Wang*, Anjun Ma*, Yuzhou Chang, Jianting Gong, Yuexu Jiang, Hongjun Fu, Cankun Wang, Ren Qi, Qin Ma*, Dong Xu*. Nat Commun 12, 1882 (2021...
Multiphysical graph neural network (MP-GNN) for COVID-19 drug design Brief. Bioinform., 23 (2022), 10.1093/bib/bbac231 Google Scholar [86] C. Zhao, H. Wang, W. Qi, S. Liu Toward drug-miRNA resistance association prediction by positional encoding graph neural network and multi-channel ...
Cell clustering for spatial transcriptomics data with graph neural networks Nat Comput Sci, 2 (2022), pp. 399-408, 10.1038/s43588-022-00266-5 View in ScopusGoogle Scholar [30] Y. Zong, T. Yu, X. Wang, Y. Wang, Z. Hu, Y. Li conST: an interpretable multi-modal contrastive learning...