CPC G06N 3/08 (2013.01) [G06F 18/213 (2023.01); G06F 18/214 (2023.01); G06F 18/24147 (2023.01); G06N 7/01 (2023.01)] | 17 Claims |
1. A method for training a node classification model, the method comprising:
obtaining, by a device comprising a memory storing instructions and a processor in communication with the memory, a target node subset and a neighbor node subset corresponding to the target node subset from a sample node set labeled with a target node class, a neighbor node in the neighbor node subset being associated with a target node in the target node subset;
extracting, by the device, a feature subset of the target node subset based on the neighbor node subset by using a node classification model, the feature subset comprising a feature vector of the target node;
performing, by the device, class prediction for the target node subset according to the feature subset, to obtain a predicted class probability subset; and
training, by the device, the node classification model with a target model parameter according to the predicted class probability subset and a target node class subset of the target node subset, by:
determining, by the device, a target loss value according to the predicted class probability subset and the target node class subset of the target node subset;
determining, by the device, a model parameter gradient according to the target loss value; and
training, by the device, the node classification model with the target model parameter according to the model parameter gradient.
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