Method and apparatus for training and using relational network embedding model

Abstract

Embodiments of the present specification provide a method of training an embedded model of a relational network, comprising obtaining a calibration node from the relational network, each calibration node having a corresponding tag value to show a credit rating of a corresponding user; then, the node characteristics of the calibration node, the neighbor node set, and the edge characteristics of each connection edge with each neighbor node being determined; determining a node embedding vector of the primary iteration of each calibration node and an edge embedding vector of the primary iterationof each connection edge based on the node feature, the edge feature and the first parameter set; a multi-level vector iteration beign then performed to determine a node embedding vector for the multi-level iteration of each calibration node; further, a prediction value of the node being determined based on a node embedding vector of a multi-level iteration of the calibration node and a set of prediction parameters; finally, each parameter value being adjusted to minimize the loss function, which is determined based on the prediction value and label value of each calibration node.

Description

Technical field [0001] One or more embodiments of this specification relate to the field of relational networks and graph embedding, and in particular to training embedding models of relational networks, and methods and devices for analyzing relational networks using the model. Background technique [0002] Credit risk is one of the basic risks faced by financial enterprises. How to effectively control credit risk is the most concerned issue of financial risk managers. With the acceleration of economic globalization and financial liberalization, as well as the rapid development of Internet finance, the risks faced by financial markets have become increasingly complex. Risk control is the foundation of the development of a financial enterprise. The risk assessment model is naturally the top priority of corporate research. A good assessment model can reduce the company's bad debts and increase the company's profitability, while also attracting more users. [0003] In the credit eval...

AI Technical Summary

Problems solved by technology

But in fact, the user data obtained by all commercial organizations is incomplete. For example, e-wallet platforms often cannot obtain the data of users' deposits and loans in banks.

Therefore, for an individual user who does not have rich data, he may not be able to enjoy due services due to his low credit evaluation score, or he may enjoy more lending rights because of his high score, which will cause certain risks to the enterprise

[0004] The traditional risk control model cannot fully utilize the value of data. The traditional model is mainly based on feature mining. According to the characteristics of different business mining and risk correlation, it uses models such as decision tree, logistic regression and instance-based deep learning to carry out risk analysis. Evaluate

Since these models only use the characteristics of each sample and cannot integrate other characteristics, they cannot fully utilize the value of data for comprehensive credit evaluation

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