Abstract: With the wide application of graph neural network (GNN) in many fields, how to extract and aggregate node features effectively has become a hot research issue. In this paper, we propose a ...
Not every sample of data can be meaningfully plotted on a two-dimensional graph. MATLAB, a technical analysis software suite from MathWorks, allows you to plot publication-quality, three-dimensional ...
Abstract: Graph Convolutional Networks (GCNs) have been widely studied for attribute graph data learning. In many applications, graph node attributes/features may contain various kinds of noises, such ...
The latest advance in recommendation shows that better user and item representations can be learned via performing graph convolutions on the user-item interaction graph. However, such finding is ...
In this paper, we propose a Hierarchical Aligned Subtree Convolutional Network (HA-SCN) for graph classification. Our idea is to transform graphs of arbitrary sizes into fixed-sized aligned graphs and ...
¹ SANKEN, The University of Osaka, Mihogaoka 8-1, Ibaraki, Osaka, 567-0047, JAPAN ² Graduate School of Information Science and Technology, The University of Osaka, Yamadaoka 1-5, Suita, 565-0871, ...
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Pinterest today shared details about how it created PinSage, a graph ...
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