Social network trust evaluation method and system based on multi-view neural network

By constructing a multi-view diagram neural network, combining the multi-head attention mechanism and attention mechanism, extracting and fusion of social attribute characteristics and trust characteristics, the problem of difficulty in obtaining trust relationship diagrams and poor feature fusion effect is solved, and a high-accurate trust evaluation is achieved.

CN119691683AActive Publication Date: 2025-03-25XI AN JIAOTONG UNIV
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Patent Information

Application Number
CN202411929127.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-25
Publication Date
2025-03-25
Estimated Expiration
2044-12-25

AI Technical Summary

Technical Problem

The existing trust relationship prediction scheme based on graph neural networks faces the problem of difficulty in obtaining trust relationship graphs and poor feature fusion effect, especially when trust relationship data is scarce or heterogeneous feature distribution.

Method used

The social network trust evaluation method based on multi-view diagram neural network is adopted. By constructing social behavior network diagrams and user trust relationship network diagrams from different perspectives, combining multiple attention mechanisms and attention mechanisms, social attribute features and trust characteristics are extracted and integrated, and multi-view features are performed to output predicted trust relationships.

Benefits of technology

It improves the accuracy and robustness of trust evaluation, and can supplement and enrich trust characteristics through social behavior data when trust relationship data is scarce or heterogeneous, so as to achieve the completion and enhancement of trust characteristics, improving the adaptability and generalization ability of the model.

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Abstract

The invention discloses a social network trust evaluation method and system based on a multi-view graph neural network, and the method comprises the steps: obtaining social network behavior information, constructing social behavior network graphs of different views, and constructing a user trust relationship network graph according to the trust relationship between users; based on social behavior network diagrams of different perspectives, a multi-head attention mechanism is built to extract influences of different types of neighbor nodes on target user node features and fuse the influences, and social attribute features are output; on the basis of the user trust relationship network diagram, utilizing an attention mechanism to fuse neighbor information of the nodes to form trust features of the user nodes; and in combination with the social attribute features and the trust features of the user nodes, performing multi-view feature fusion, and outputting a predicted trust relationship. The problems that a traditional trust evaluation method depends on a predefined model and is poor in universality, and an existing trust relation prediction scheme based on a graph neural network is difficult to obtain a trust relation graph and is poor in feature fusion effect are solved.
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