MA-STS-Based Social Intimacy Analysis Algorithm Using Real Campus Network Data

Author:

Zeng Yifu1ORCID,Qi Xiangshu1,Yang Weiping2,Li Jiatao1,Pan Nian3,Chen Guo3ORCID

Affiliation:

1. College of Computer Science and Engineering, Changsha University, Changsha 410022, Hunan, China

2. Center for Information of Education Management, Beijing 100816, China

3. College of Computer Science and Electronic Engineering, Hunan University, Changsha 410012, Hunan, China

Abstract

In recent years, the widespread availability of Wi-Fi in various settings, including universities, enterprises, and large shopping centers, has become increasingly prevalent. The user’s time and location information embedded in wireless network systems can reveal individual and group social relationships, which indirectly reflect each person’s psychological well-being. However, due to challenges in obtaining complete data, the high complexity of related data, and the absence of suitable data analysis models, few studies have analyzed student social behavior using data from university campus networks. This paper employs real-world data from a renowned Chinese university’s wireless campus network for in-depth analysis and introduces a novel multiangle semantic trajectory similarity (MA-STS) algorithm to infer the intimacy and relationship types (such as teacher-student, friends, classmates, or romantic partners) between users. The experiments demonstrate that the proposed algorithm achieves an accuracy of over 95%.

Funder

Natural Science Foundation of Hunan Province

Publisher

Hindawi Limited

Subject

Artificial Intelligence,Human-Computer Interaction,Theoretical Computer Science,Software

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