Information diffusion prediction method based on time sequence hypergraph attention neural network

Abstract

The invention discloses an information diffusion prediction method based on a time sequence hypergraph attention neural network. Information diffusion is predicted by learning preference of a user from two aspects of a static friendship network and a dynamic interaction network of the user. According to the method, not only is a static dependency relationship of a user captured from a friendship network of the user by using a graph convolutional neural network, but also a hypergraph attention network is innovatively designed, so that interaction of the user on a cascade level and connection between cascades are dynamically learned from a serialized information diffusion hypergraph. And according to the cascade characteristics to be predicted, an embedded search module searches vectors of corresponding users from the obtained user representation vectors in the two aspects so as to carry out the next step of interactive learning. And finally, two self-attention modules are utilized to respectively carry out internal deep interactive learning on the cascade representation obtained from the two aspects to predict the next influenced user, so that gradual prediction of network information diffusion is realized.

Description

technical field [0001] The invention belongs to the field of information diffusion prediction and relates to an information diffusion prediction method based on a temporal hypergraph attention neural network. Background technique [0002] The rise of online social media has accelerated the speed of information sharing and dissemination, so efficient information diffusion prediction techniques are urgently needed to handle emerging task scenarios, such as false information control, hotspot detection, and online recommendation. Current typical information diffusion prediction methods can be divided into three categories: feature engineering-based methods, generation-based methods, and representation learning-based methods. Among them, the method based on feature engineering predicts the popularity of information at the macro level by extracting representative features in the process of information diffusion. However, this approach is difficult to model the dependencies among ...

AI Technical Summary

Problems solved by technology

Although the graph convolutional neural network has a good ability to encode graph structure features, the basic graph convolutional network cannot accurately capture the differential characteristics of the influence of user neighbors in social networks, and cannot describe the dynamic behavior of the information diffusion process.

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