Group Relationship Mining of College Students Based on Predictive Social Network

Huazhang Liu,Jian Su

doi:10.1155/2022/5443655

Abstract

With the rapid development of the Internet, social networks have shown an unprecedented development trend among college students. Closer social activities among college students have led to the emergence of college students with new social characteristics. The traditional method of college students’ group classification can no longer meet the current demand. Therefore, this paper proposes a social network link prediction method-combination algorithm, which combines neighbor information and a random block. By mining the social networks of college students’ group relationships, the classification of college students’ groups can be realized. Firstly, on the basis of complex network theory, the essential relationship of college student groups under a complex network is analyzed. Secondly, a new combination algorithm is proposed by using the simplest linear combination method to combine the proximity link prediction based on neighbor information and the likelihood analysis link prediction based on a random block. Finally, the proposed combination algorithm is verified by using the social data of college students’ networks. Experimental results show that, compared with the traditional link prediction algorithm, the proposed combination algorithm can effectively dig out the group characteristics of social networks and improve the accuracy of college students’ association classification.

Highlights

Nowadays, the rapid development of the Internet affects people’s work, life, and study
We use the simplest linear method to combine the proximity link prediction based on neighbor information with the likelihood analysis link prediction based on a random block, which is called the combination algorithm
Where S1 represents the proximity link prediction based on neighbor information, S2 represents the likelihood analysis link prediction based on a random block, and λ ∈ [0, 1]

Summary

Introduction

The rapid development of the Internet affects people’s work, life, and study. Ma et al [18] proposed a link prediction method based on structural similarity information and community information for the Twitter network Experiments show that this method can be effectively used in large-scale directed and asymmetric networks. Kagan et al [19] put forward a new link prediction algorithm based on the maximum likelihood model, combining the interest characteristics of nodes and the network structure characteristics, and achieved a better bidirectional edge division effect. E main innovation of this study is that, unlike the traditional social network link prediction method, we try to combine proximity analysis with likelihood analysis so as to improve the mining system and improve the classification accuracy and provide a reference for the further development of the follow-up mining system The relationship among college students on social networks is investigated from the perspective of link prediction, and the social network link prediction model is constructed by taking community division as the research object. is study combines proximity link prediction and likelihood analysis link prediction, trying to find the most suitable link prediction method for college students so as to improve the accuracy of college students’ group classification. e main innovation of this study is that, unlike the traditional social network link prediction method, we try to combine proximity analysis with likelihood analysis so as to improve the mining system and improve the classification accuracy and provide a reference for the further development of the follow-up mining system

Student Group Identification Based on Social Network

Student Group Identification Based on Social Network Link Prediction

Experiment and Result Analysis

Conclusions