Citation network node classification method and system for relationship uncertainty
An uncertainty, network node technology, applied in the direction of text database clustering/classification, biological neural network model, other database indexes, etc. Effects of Deterministic Problems
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Embodiment 1
[0037] This embodiment provides a citation network node classification method for relationship uncertainty;
[0038] Such as figure 1 As shown, the citation network node classification method for relationship uncertainty includes:
[0039] S101: Obtain papers with class labels to be predicted, and obtain citation networks with known class labels;
[0040] S102: Construct a meta-path neighbor graph according to the papers of the category labels to be predicted and the citation networks of the known category labels;
[0041] S103: Generate several generalization graphs based on the meta-path neighbor graph;
[0042] S104: Input all the generalization graphs into the pre-trained graph convolutional neural network, and output the category label of the paper to be predicted category label.
[0043] As one or more embodiments, said S102: Construct a meta-path neighbor graph according to the papers of the category label to be predicted and the citation network of the known categor...
Embodiment 2
[0095] This embodiment provides a citation network node classification system for relationship uncertainty;
[0096] A classification system for citation network nodes for relationship uncertainty, including:
[0097] An acquisition module configured to: acquire papers with category labels to be predicted, and obtain citation networks with known category labels;
[0098] A building module configured to: construct a meta-path neighbor graph according to the papers of the category label to be predicted and the citation network of the known category label;
[0099] A generating module configured to: generate several generalization graphs based on the meta-path neighbor graph;
[0100] The output module is configured to: input all the generalization graphs into the pre-trained graph convolutional neural network, and output the category labels of papers to be predicted category labels.
[0101] What needs to be explained here is that the above acquisition module, construction mod...
Embodiment 3
[0105] This embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein, the processor is connected to the memory, and the one or more computer programs are programmed Stored in the memory, when the electronic device is running, the processor executes one or more computer programs stored in the memory, so that the electronic device executes the method described in Embodiment 1 above.
[0106] It should be understood that in this embodiment, the processor can be a central processing unit CPU, and the processor can also be other general-purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate array FPGA or other programmable logic devices , discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, o...
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