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Identifying influential users using homophily-based approach in location-based social networks

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Abstract

Today, with the expansion of online social networks and their impact on various aspects of human life, investigating the interactions between users and identifying influential users for various advertising applications and accelerating or preventing the dissemination of information has been the focus of researchers. One of the fundamental researches is investigating the fact that the similarity of users’ characteristics along with their interests leads to new relationships in the friendship network, a concept known as homophily. The study of homophily can provide significant insight into the flow of information and behaviors in a community to analyze the formation of online communities. In recent years, the emergence of location-based social networks (LBSNs) has created massive datasets by sharing spatial and temporal information better than ever before. This issue enables researchers to analyze the behavioral patterns of users and their impact on their social connections and friends. Throughout the present paper, a framework is being defined to examine the effect of combining structural similarity and homophily in determining users’ social influence under two scenarios. The experiments simulate performance of nodes on three LBSNs: Gowalla, Foursquare, and Brightkite. By calculating the correlation coefficient for the similarity methods applied, it can be displayed that with the increase in homophily, the correlation of the proposed method and the social influence increases. A new measure of centrality is also introduced by using the topological structure of the user's communication network, such as the eigenvector centrality along with the values of friendship influence and the number of spatial movements of the user. The results show that our proposed centrality matches up to 85% with baseline methods.

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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Authors and Affiliations

  1. Department of Computer Engineering, Qom Branch, Islamic Azad University, Qom, Iran

    Zohreh Sadat Akhavan-Hejazi & Mostafa Ghobaei-Arani

  2. Department of Computer Engineering, Kashan Branch, Islamic Azad University, Kashan, Iran

    Mahdi Esmaeili

  3. School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran

    Behrouz Minaei-Bidgoli

Authors
  1. Zohreh Sadat Akhavan-Hejazi
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  2. Mahdi Esmaeili
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  3. Mostafa Ghobaei-Arani
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  4. Behrouz Minaei-Bidgoli
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Contributions

Zohreh sadat Akhavan Hejazi, Mehdi Esmaeli, Mostafa Ghobaei-Arani, and Behrooz Minaee Bidgoli conducted this research. Zohreh sadat Akhavan Hejazi was involved in methodology, software, validation, writing original draft. Mehdi Esmaeli and Mostafa Ghobaei-Arani assisted in conceptualization, supervision, writing review & editing, formal analysis, project administration. Behrooz Minaee Bidgoli helped in investigation, resources, data curation, visualization.

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Correspondence to Mostafa Ghobaei-Arani.

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Akhavan-Hejazi, Z.S., Esmaeili, M., Ghobaei-Arani, M. et al. Identifying influential users using homophily-based approach in location-based social networks. J Supercomput (2024). https://doi.org/10.1007/s11227-024-06228-0

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