Two-Stage User Identification Based on User Topology Dynamic Community Clustering

Jiajing Zhang,Zhenhua Yuan,Neng Xu,Jinlan Chen,Juxiang Wang,M Irfan Uddin

doi:10.1155/2021/5567351

Abstract

In order to solve the problem of node information loss during user matching in the existing user identification method of fixed community across the social network based on user topological relationship, Two-Stage User Identification Based on User Topology Dynamic Community Clustering (UIUTDC) algorithm is proposed. Firstly, we perform community clustering on different social networks, calculate the similarity between different network communities, and screen out community pairs with greater similarity. Secondly, two-way marriage matching is carried out for users between pairs of communities with high similarity. Then, the dynamic community clustering was performed by resetting the different community clustering numbers. Finally, the iteration is repeated until no new matching user pairs are generated, or the set number of iterations is reached. Experiments conducted on real-world social networks Twitter-Foursquare datasets demonstrate that compared with the global user matching method and hidden label node method, the average accuracy of the proposed UIUTDC algorithm is improved by 33% and 26.8%, respectively. In the case of only user topology information, the proposed UIUTDC algorithm effectively improves the accuracy of identity recognition in practical applications.

Highlights

With the rapid development and application of artificial intelligence technology, the application scope of artificial intelligence technology is expanding
Artificial intelligence technology represented by deep learning and breadth learning is becoming more mature than before
Among the methods based on multidimensional information fusion, Peled et al [14] extracted two aspects of user topology and user attributes, established a 27-dimensional feature vector, and judged whether the user identity matches through the similarity of the feature vector

Summary

Introduction

With the rapid development and application of artificial intelligence technology, the application scope of artificial intelligence technology is expanding. Data integration is the premise and foundation of breadth learning. In order to achieve multisource data integration, user identification across social network has become a very valuable research hotspot. User information is scattered in different networks, and the same real user information cannot be shared between different networks. In order to break the phenomenon of information “islands” and achieve multisource data integration, cross-social network identification is a necessary premise and basis. Cross-social network user identification has strong research value and practical application significance in many fields such as user portraits, commercial advertising, friend recommendation, and maintenance of online public opinion security. Cross-social network user identification methods mainly include methods based on user attribute information, user behavior information, and user topology. Erefore, this article decides to use user topology information for user identification User topology, namely, friend relationships, is authentic and difficult to forge [2]. erefore, this article decides to use user topology information for user identification

Related Work

UIUTDC Algorithm

Calculate and Filter Out the Community Pairs with

Experimental Results and Analysis