Unbalanced node classification method based on minority class weighted graph neural network
A neural network and node balancing technology, applied in the field of machine learning, can solve problems such as the decline of classification effect and the convergence of node characteristics, so as to achieve the effect of reducing bias, accelerating convergence speed and improving classification performance
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[0043]The technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Apparently, the described embodiments are some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.
[0044] Such as figure 1 As shown, this embodiment discloses a method for classifying unbalanced nodes based on a minority class weighted graph neural network, comprising the following steps:
[0045] Step 1: For the input graph structure data, calculate the node membership value based on the adjacency information to obtain the weighted feature information of the node in the embedding space;
[0046] Specifically, in step 1, the node membership is calculated using the adjacency information of the nodes and the ...
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