time complexitymodelTime complexity of an algorithm is closely related to its implement method. In order to make the time complexity analysis more universal in engineering, the Operator Cost Model (OCM) was proposed and used for analyzing the Graph-Based Segmentation Algorithm (GBSA) in this paper...
Given the prevalence of large-scale graphs in real-world applications, the storage and time for training neural models have raised increasing concerns. 鉴于大规模图在实际应用中的流行,训练神经模型的存储和时间引起了越来越多的关注。 However, the prevalence of large-scale graphs in real-world scenarios...
whereεis the learning rate andLis the graph Laplacian. Computing theLX(elastic forces) term has time complexityO(N) for sparse graphs, andO(N2) for dense graphs.VNNis repulsive energy helping avoid node overlap, with complexityO(N2), which can be decreased to\(O(N\log N)\)by the Barn...
In the study, 3D printers of a single material are taken mainly as the study objects (note: new printers with dual-nozzle or multi-nozzle for printing different materials at the same time are already currently marketed, but such printers are not included in the category of this study). The...
Leveraging class information for initializing the mapping can effectively accelerate the learning process. At the same time, to guarantee stronger numerical stability for M and associated computations, we introduce a row-wise normalization step as the following: 映射矩阵使每个原始节点能够由合成节点的加权...
They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this Review, we provide an overview of the ...
Graph convolutional network(GCN) provides a powerful means for graph-based semi-supervised tasks. However, as a localized first-orderapproximationof spectralgraph convolution, the classic GCN can not take full advantage ofunlabeled data, especially when the unlabeled node is far from labeled ones. To...
But limited by model complexity, most STGNNs only consider short-term historical MTS data, such as data over the past one hour. However, the patterns of time series and the dependencies between them (i.e., the temporal and spatial patterns) need to be analyzed based on long-term ...
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...
The above-mentioned methods primarily rely on global time information for modeling temporal attributes but do not take into account the more intricate changes in local information of nodes within dynamic networks. Therefore, this study needs to consider modeling temporal attributes from the perspective ...