例子: >>>importnumpyasnp>>>fromscipy.statsimportmultiscale_graphcorr>>>x = np.arange(100)>>>y = x>>>res =multiscale_graphcorr(x, y)>>>res.statistic, res.pvalue (1.0,0.001) 要运行不配对的两个样本测试, >>>x = np.arange(100)>
GRAPH neural networksARTIFICIAL neural networksBINDING sitesPROTEIN structureProtein-Protein Interactions (PPIs) involves in various biological processes, which are of significant importance in cancer diagnosis and drug development. Computational based PPI prediction methods are more preferre...
这篇文章提出了一种基于多尺度图卷积网络的3D人体运动预测方法——dynamic multiscale graph neural network (DMGNN)。该网络结构分为编码器和解码器,其中编码器由一系列多尺度图计算单元(multiscale graph computational unit,MGCU)组成,解码器由图时序门单元(graph-based GRU)组成。该方法通过MGCU提取不同尺度的人...
Therefore, a multiscale graph based spatio-temporal graph convolutional network (MG-STGCN) method is proposed to predict the energy consumption of natural gas pipelines. MG-STGCN innovatively uses multiple GCN to capture the parameter correlation of the pipeline and the spatial dependence of the ...
This diffusion operator P is raised to tD, the PHATE optimal diffusion timescale as computed by von Neumann entropy, to simulate a tD-step random walk over the data graph.(算法的内容还是有点难以理解)。 (3)Finally, by taking logarithm of P^{t_{D}} , we calculate the diffusion potential...
This is a C++ implementation of the Multiscale Graph Laplacian kernel as described in: R. Kondor, H. Pan, The Multiscale Graph Laplacian (2016) Requirements C++11 Eigen Installation/Setup Change the EIGENDIR variable Makefile.options to the path to your installation of the Eigen library. Run...
Visibility graph has established itself as a powerful tool for analyzing time series. We in this paper develop a novel multiscale limited penetrable horizontal visibility graph (MLPHVG). We use nonlinear time series from two typical complex systems, i.e.
A multiscale graph theory-based approach is introduced here to predict the microscale crack path in polycrystalline materials. The crack path is represented as the boundary of the partition of a geometric graph. The partitioning is carried out by optimizing an Ising-type hamiltonian. The hamiltonian...
Multiscale Graph Neural Networks in PyTorch PyTorch implementation of Community Detection with Graph Neural Networks [1]. For a high-level introduction to Graph Neural Networks, see: Thomas Kipf, Graph Convolutional Networks (2016) Note: This code is based on the Lua implementation in https://git...
Visibility graph has established itself as a powerful tool for analyzing time series. We in this paper develop a novel multiscale limited penetrable horizontal visibility graph (MLPHVG). We use nonlinear time series from two typical complex systems, i.e., EEG signals and two-phase flow signals...