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Abstract

A method for embedding a data network graph includes: performing node feature extraction on a data network graph and a negative sample network graph using a first network embedding model, to obtain a positive sample embedding vector and a negative sample embedding vector performing node feature extraction on a first enhanced graph and a second enhanced graph of the data network graph using the first network embedding model, to obtain a first global embedding vector and a second global embedding vector; determining a first matching degree and a second matching degree; adjusting a parameter of the first network embedding model based on a loss value determined based on the first matching degree and the second matching degree; and performing node feature extraction on the data network graph based on an adjusted first network embedding model, to obtain an embedding vector configured for classifying each node in the data network graph.

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