Dynamic community discovery method based on recurrent convolutional neural network and auto-encoder

Abstract

The invention relates to a dynamic community discovery method based on a recurrent convolutional neural network and an auto-encoder. The method comprises the following steps: firstly, constructing a network spatial feature learning model based on a convolutional neural network, and learning spatial topological features of the network to obtain a network spatial feature vector; secondly, fusing a network spatial feature learning model based on a convolutional neural network, constructing a network spatial-temporal feature learning model based on a recurrent neural network, the convolutional neural network and an auto-encoder by taking a network spatial feature vector as an input of the model, and learning spatial-temporal features of the network to obtain a network spatial-temporal featurevector; and finally, community discovery is performed on the basis of the network space-time feature vector so as to detect the dynamic community structure of the social network. The method can be applied to analyzing the social network, autonomously learning and extracting the spatial and temporal features of the social network, and can further improve the modularity of a community structure, thereby revealing the topological structure and the like of a real network, and further effectively predicting network user behaviors, information propagation and the like.

Description

technical field [0001] The invention relates to a dynamic community discovery method based on a recursive convolutional neural network and an autoencoder. Background technique [0002] With the development of the Internet, especially the mobile Internet, social networking platforms for making friends and sharing information are developing rapidly. On domestic and foreign social network platforms represented by Sina Weibo, WeChat, Taobao, Twitter and Facebook, people can express their opinions, make friends and interact, disseminate information and sell products, etc. According to Facebook's report in the first quarter of 2018, an average of 2.2 billion users use Facebook every month, the number of daily active users is as high as 1.4 billion, and an average of 5 new accounts are created every second. In addition, the number of monthly active users of Wechat, a popular domestic social software, also exceeded the 1 billion mark for the first time in 2018. [0003] Online soc...

AI Technical Summary

Problems solved by technology

[0006] First of all, the massive number of nodes puts forward extremely strict requirements on the performance of community discovery algorithms in complex social networks. Only algorithms with linear time complexity can run on real social networks and carry out relevant analysis and applications. At present, there are only a few algorithms Meet linear or near-linear time complexity requirements

Images