Network representation learning method and device and storage medium

Abstract

The invention discloses a network representation learning method and device and a storage medium, and the method comprises the steps of obtaining the structure information of each node in a network, and building a structure transfer matrix based on the structure information; obtaining the attribute information of each node in the network, and establishing an attribute transfer matrix based on theattribute information; fusing the structure transfer matrix and the attribute transfer matrix to obtain a bias transfer matrix; sampling each node in the bias transfer matrix to obtain a plurality ofwalking sequences; and establishing a neural network model, and inputting the walking sequence into the neural network model for network representation learning to obtain a representation vector of anode corresponding to the walking sequence. According to the present invention, the information of different sources can be seamlessly combined, the calculation complexity is reduced, and the networkrepresentation quality is improved, so that the effect of the representation vector on a network analysis task is enhanced.

Description

technical field [0001] The present invention relates to the technical field of network analysis, in particular to a method, device and storage medium for network representation learning. Background technique [0002] Network structure is a broad representation of data, and information networks have become ubiquitous in practical applications, such as social networks, citation networks, biological networks, etc. In the era of big data, the network has become an important medium for effectively storing and exchanging entity relationship knowledge. Mining knowledge in network data has attracted continuous attention from academia and business. [0003] At present, the research on network representation learning has developed from traditional matrix eigenvector calculation to deep learning algorithm based on random walk and network representation learning combined with external information. However, in the era of data explosion, there are hundreds of millions of nodes in a large...

AI Technical Summary

Problems solved by technology

However, in the era of data explosion, there are hundreds of millions of nodes in a large network structure. Due to the problem of computational complexity, the method based on matrix decomposition cannot be applied in reality, and the method based on deep learning cannot be combined reasonably and effectively. Data from different sources

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