Neighborhood exploration method based on heterogeneous graph neural network
A neural network and heterogeneous technology, applied in the field of neighborhood exploration based on heterogeneous graph neural network, can solve problems such as poor extraction of potential features of users or products, weakening of structural equivalence, fragmentation, etc., to improve reliability Interpretability and Accuracy Effects
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[0019] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, the specific implementation manners of the present invention will now be described with reference to the accompanying drawings.
[0020] In this example, if figure 1 As shown, a neighborhood exploration method based on a heterogeneous graph neural network includes the following steps:
[0021] Step 1: Define a parameter α used to guide the direction of exploration;
[0022] Step 2: In a given heterogeneous graph ɡ and meta-path Φ, for node V i Each immediate neighbor of V j , with a probability of α do not perform any operation, and jump to the next direct neighbor node; with a probability of 1-α, carry out biased walk sampling, and use the meta-path neighbors obtained by the walk sampling Replace the original immediate neighbor V j ;
[0023] Step 3: With the probability of α, add the immediate neighbors to the neighbor set; with the probability of 1...
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