Node representation learning method fusing diffusion hypergraph modeling and graph convolutional network

By fusing diffusion hypergraph modeling with graph convolutional networks, and combining an improved LSTM network with a hypergraph attention network, the problem of temporal and global information not being learned in deep learning in information diffusion modeling is solved. This achieves a refined and dynamic representation of complex information propagation behavior, improving prediction accuracy and adaptability.

CN121052284APending Publication Date: 2025-12-02KUNMING UNIV OF SCI & TECH
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Patent Information

Application Number
CN202511194260.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-08-25
Publication Date
2025-12-02

AI Technical Summary

Technical Problem

Existing technologies fail to deeply learn temporal information and integrate global information in information diffusion modeling, making it difficult to effectively capture complex temporal dependencies and high-order structural features.

Method used

We employ a method that combines diffusion hypergraph modeling with graph convolutional networks. By extracting the static node structure representation of the social graph through graph convolutional networks, and combining an improved LSTM network and a hypergraph attention network, we perform feature fusion and embedding of temporal feature information to achieve joint modeling of temporal dependencies and inter-node topology in the information diffusion path.

Benefits of technology

It significantly enhances the ability to express complex information propagation behavior, improves the accuracy of node classification and diffusion behavior prediction, and adapts to data graphs of different sizes and structures, demonstrating strong versatility and engineering application value.

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Abstract

The invention relates to a node representation learning method fusing diffusion hypergraph modeling and a graph convolutional network, and belongs to the technical field of modeling and prediction of a complex diffusion process. The method comprises the following steps: extracting a static node structure representation of a social graph, and obtaining a social context feature representation of the static node structure representation; constructing a diffusion hypergraph according to the propagation diffusion sequence, and dividing the diffusion hypergraph into a plurality of sub diffusion hypergraphs; performing feature extraction and fusion on each sub-diffusion hypergraph to obtain user information features; obtaining time sequence feature information embedding based on the improved LSTM network and the user information features; and performing cross-dimension splicing on the social context feature representation and the time sequence feature information to obtain a splicing fusion feature, obtaining a propagation probability based on the splicing fusion feature, and completing model performance evaluation based on the propagation probability. The objective of the invention is to solve the technical problems that time sequence information is not deeply learned and global information is not integrated in the prior art.
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