Node feature construction method based on honey point intelligence and neighborhood weight adaptive updating

By introducing honey point intelligence and adaptive weight update mechanisms into the graph neural network, the problem of differentiation of node characteristics and insufficient timeliness in the traceability graph is solved, and a more accurate and flexible feature structure is achieved to adapt to complex dynamic graph structures.

CN119670810AActive Publication Date: 2025-03-21GUANGZHOU UNIVERSITY
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Patent Information

Application Number
CN202411678816.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-22
Publication Date
2025-03-21
Estimated Expiration
2044-11-22

AI Technical Summary

Technical Problem

When existing graph neural networks deal with traceability graph scenarios, the differentiated representation ability of node features and the timeliness of feature expression are insufficient, making it difficult to capture complex neighborhood relationships and adapt to dynamic graph changes.

Method used

The node feature construction method based on honey point intelligence and neighbor weight adaptive update is adopted. The influence of neighbor nodes of different classes is dynamically perceived in real time, and adaptive adjustments are made based on graph structure information to dynamically adjust the weight of each order of neighbor features in the attention score.

Benefits of technology

It significantly improves the differentiated expression ability and timeliness of node characteristics, and can more accurately reflect the real-time changes of nodes and edges in the traceability graph, and is suitable for complex and dynamic graph structure environments.

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Abstract

The invention discloses a node feature construction method based on honey point intelligence and neighborhood weight adaptive updating. The method comprises the following steps: firstly, performing feature extraction on nodes and edges input into a traceability graph to generate an initial feature vector of each node; secondly, aggregating initial feature vectors of neighbor nodes in an L-order range of target nodes in the traceability graph by adopting a mean value aggregation method, and generating a neighbor feature matrix with a fixed dimension; and finally, combining the neighbor feature matrix with the initial feature vector of the target node by adopting a feature updating method based on an attention mechanism to update, and introducing a neighborhood adaptive weight to dynamically adjust the weight of each order of neighborhood feature in the attention score to obtain the feature of the updated target node. Therefore, the weight is dynamically adjusted in the feature aggregation process, and the real-time change of the nodes in the graph is fused into feature updating, so that the node features can reflect the dynamic change of a scene in time, and the differential expression capability of the node features is remarkably improved.
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