Method and apparatus for embedding data network graph, computer device, and storage medium

A computer device, comprising a memory and at least one processor, the memory containing a computer program that, when being executed, causes the at least one processor to implement:

• performing node feature extraction on the 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, the data network graph being a positive sample network graph and being an imbalanced network graph constructed based on an imbalanced object dataset;
• 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 first matching degrees between the positive sample embedding vector and the first global embedding vector as well as the second global embedding vector, and determining second matching degrees between the negative sample embedding vector and the first global embedding vector as well as the second global embedding vector;
• determining a loss value based on the first matching degrees and the second matching degrees, and adjusting a parameter of the first network embedding model based on the loss value;
• 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 a node in the data network graph.

A non-transitory computer-readable storage medium containing a computer program that, when being executed, causes at least one processor to implement:

• performing node feature extraction on the 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, the data network graph being a positive sample network graph and being an imbalanced network graph constructed based on an imbalanced object dataset;
• 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 first matching degrees between the positive sample embedding vector and the first global embedding vector as well as the second global embedding vector, and determining second matching degrees between the negative sample embedding vector and the first global embedding vector as well as the second global embedding vector;
• determining a loss value based on the first matching degrees and the second matching degrees, and adjusting a parameter of the first network embedding model based on the loss value;
• 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 a node in the data network graph.