In this paper, we propose to design a generic represen- tation of skeleton sequences for action recognition by ex-tending graph neural networks to a spatial-temporal graph model, called Spatial-Temporal Graph Convolutional Net-works (ST-GCN). GitHub is where people build software. There has been an increasing research interest in deep learning on graph structured data, e.g., [3, … Currently, most graph neural network models have a somewhat universal architecture in common. Previously, Kipf and Welling presented a simplified layer-wise graph neural network model (GCN) [11].

More than 40 million people use GitHub to discover, fork, and contribute to over 100 million projects. 2016) offers two combinations. The application of GCNs to model dynamic graphs over large-scale datasets, e.g. In order to tackle the above challenges, we propose a nov- However, these methods still fail to simultaneously model the spatial-temporal features and dynamic correlations of traffic data. Disentangled Graph Convolutional Networks Jianxin Ma 1Peng Cui Kun Kuang Xin Wang 1Wenwu Zhu Abstract The formation of a real-world graph typically arises from the highly complex interaction of many latent factors. ta; graph convolutional neural networks (GCN) are used for describing spatial correlation of graph-based data. human skeleton se-quences, is yet to be explored. Graphs or networks can be used to model any interactions between entities such as social interactions (Facebook, Twitter), biological networks (protein-protein interaction), and citation networks. Learning Dynamic Hierarchical Topic Graph with Graph Convolutional Network for Document Classi cation Zhengjue Wang* 1, Chaojie Wang* , Hao Zhang , Zhibin Duan , Mingyuan Zhou2, Bo Cheny1 1National Laboratory of Radar Signal Processing, Xidian University, Xi’an, China 2McCombs School of Business The University of Texas at Austin, Austin, TX 78712, USA Event Prediction; Dynamic Graph Convolutional Network; Tempo-ral Encoding ACM Reference Format: ... Graph convolutional networks utilize the adjacency matrix or the Lapla-cian matrix to depict the structure of a graph and capture spatial features between the nodes. I will refer to these models as Graph Convolutional Networks (GCNs); convolutional, because filter parameters are typically shared over all locations in the graph (or a subset thereof as in Duvenaud et al., NIPS 2015).

whereby the former digest graph information and the latter handle dynamism. Graph Convolutional Neural Networks for Optimal Load Shedding under Line Contingency Cheolmin Kim Department of Industrial Engineering and Management Sciences Northwestern University, Evanston, IL, USA Email: [email protected] Kibaek Kim Prasanna Balaprakash Mihai Anitescu Mathematics and Computer Science Division Argonne National Laboratory, Lemont, IL, USA Email: … The most explored GNNs in this context are of the convolutional style and we call them graph convo-lutional networks (GCN), following the terminology of the related work, although in other settings GCN specifically refers to the architecture proposed by (Kipf and Welling 2017). GCRN (Seo et al.