2. Heterogeneous Temporal Graph Neural Network (HTGNN) 2.1 深度学习与图神经网络简介 深度学习是一种机器学习方法,它通过多层神经网络模拟人类大脑的工作原理来进行模式识别和预测。图神经网络是一种特殊类型的深度学习模型,用于处理图数据。传统上,深度学习主要用于处理向量化的数据,例如图像和文本等。然而,在许多现...
In this paper, we aim at the above problems by exploring a Spatio-Temporal Heterogeneous Graph Neural Network With Multi-View Learning Framework(MVJGL) for traffic prediction. In particular, we first model different types of traffic features and construct multiple graph structures. Then, we design...
1.1 Heterogeneous Graph 异构图 1.2 Graph Neural Networks 二、研究目的 三、Heterogeneous Graph Transformer 3.1 总览 3.2 Heterogeneous Mutual Attention 3.3 Heterogeneous Message Passing 3.4 Target-Specific Aggregation 3.5 Relative Temporal Encoding 四、参考 论文其余的部分对于我不是很重要,省略 论文链接:arxiv...
Recently, graph neural networks (GNNs) have been successfully used to model interaction and spatio-temporal information in POI recommendations, but the data sparsity of POI recommendations affects the training of GNNs. Although some existing GNN-based POI recommendation approaches try to use social ...
Recently, with graph neural networks (GNNs) becoming a powerful technique for graph representation, many excellent GNN-based models have been proposed for
Taxi demand forecasting based on the temporal multimodal information fusion graph neural network 2022, Applied Intelligence Smartphone App Usage Analysis: Datasets, Methods, and Applications 2022, IEEE Communications Surveys and Tutorials View all citing articles on Scopus ...
Amalgamating Knowledge from Heterogeneous Graph Neural Networks Yongcheng Jing1, Yiding Yang2, Xinchao Wang3,2, Mingli Song4, Dacheng Tao1 1The University of Sydney, 2Stevens Institute of Technology, 3National University of Singapore, 4Zhejiang University {yjin9495, dachen...
Cloud Learning 推荐系统 19KDD MEIRec Metapath-guidedHeterogeneousGraphNeuralNetworkfor Intent... Abstraction viaGraphAttentionNeuralNetwork交通/城市 19AAAI Spatiotemporal Multi‐GraphConvolutionNetwork 智能推荐 Type-Aware Anchor Link Prediction across Heterogeneous Networks Based on Graph Attention Network ...
In this work, we introduce stKeep, a graph embedding method that integrates multimodal data (i.e., histology, gene expression, spatial location, and histological regions) and gene-gene interactions (i.e., GRN, PPI, and LRP), to dissect TME heterogeneity by identifying cell-modules, gene-mod...
(i\). The reason is that, in bulk ATAC-seq data, it is observed that many highly expressed genes will also have ATAC-seq peaks in the exon regions, mainly due to the temporal PolII and other transcriptional machinery bindings. Based on that observation, to better fit the model with ...