Patent ID: 11915484
Assignee: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 1:
2. The method according to claim 1, wherein the graph convolutional neural network comprises a first graph convolutional layer and a second graph convolutional layer, and wherein:
the first graph convolutional layer comprises at least one sample node representing a sample and at least one proxy node representing a set of samples, and sample nodes belonging to a given set of samples are unidirectionally connected to a given proxy node, and proxy nodes are interconnected, and each proxy node performs a weighted sum on sample features of sample nodes connected to the each proxy node to obtain a proxy feature of the each proxy node, and proxy features of all proxy nodes are fused through the first graph convolutional layer to obtain output features of the proxy nodes of the first graph convolutional layer; and
the second graph convolutional layer comprises at least one sample node representing a sample and at least one proxy node representing a set of samples, and sample nodes belonging to a given set of samples are bidirectionally connected to a given proxy node, and proxy nodes are interconnected, and the output features of the proxy nodes of the first graph convolutional layer are fused through the second graph convolutional layer to obtain an output feature of each sample node.