Network characterization method based on adversarial attention mechanism

Abstract

The invention relates to a network characterization method based on an adversarial attention mechanism. The method comprises a double-mapping-function model, the first mapping function is used for distributing different weights to different node pairs through a graph attention network according to node attribute information and network topology information of real data, and an original network ismapped to a low-dimensional space to obtain more accurate low-dimensional expression of the real data; the second mapping function is node attribute information and network topology information whichare obtained by combining the obtained low-dimensional expression of the real data with disturbance to obtain noise and inputting the noise into a generator to be mapped into noise; the two functionsare used as two tuples to be input into a discriminator to be discriminated, optimization of a generator and an encoder is carried out through a result given by the discriminator, and finally low-dimensional expression which is good in robustness and capable of completely storing original network information is obtained. According to the method, the graph attention network is adopted for network representation, the correlation degree between different nodes is considered, the method is closer to the actual situation, and the effect is better.

Description

technical field [0001] The technical solution of the present invention relates to a network representation model (expressing network characteristics in a low-dimensional space) with an adversarial attention mechanism to facilitate the realization of subsequent network analysis tasks, such as node classification tasks, link prediction and other tasks, specifically a A Network Representation Method Based on Adversarial Attention Mechanism. Background technique [0002] With the continuous development of the Internet, various Internet-based platforms (such as social platforms, e-commerce platforms) are integrating into people's lives. Users have gradually changed from information acquirers to information producers, and it has become a very common phenomenon to post comments on social platforms and purchase items on e-commerce platforms. All these behaviors have accumulated a large amount of information on the Internet, and functions such as product recommendation and friend re...

AI Technical Summary

Problems solved by technology

The present invention overcomes the defects of insufficient use of node information and inappropriate use of model mechanism in the prior art, and the defect that the prior art does not improve the problems existing in this method

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