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Triangle-induced and degree-wise sampling over large graphs in social networks

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Abstract

Social networks are crucial channels for information dissemination because they facilitate the effective exchange of ideas and information. The extensive utilization of these networks in daily life results in their explosive growth. Most methods are not practical for large-scale network analysis. Hence, analyzing a small portion instead of the whole network could be preferable, a technique known as graph sampling. According to a literature review, many approaches to graph sampling focus only on representing connectivity from triangle count assessment or node degree evaluation rather than both. This paper introduces an innovative method called TDGS (triangle-induced and degree-wise graph sampling) for producing a sample under the impact of both triangle count and node degree in social networks. The key idea behind TDGS is that it uses a centrality measure based on the degree centrality and local information of nodes about the connectivity of their neighbors to guide the sampling process. Furthermore, TDGS proposes a distributed model that can handle large-scale graphs. We evaluate the performance of our proposed method using real-world social networks. The experimental results show that TDGS provides significantly more precise information about node degrees than the well-known graph sampling methods and can estimate the global clustering coefficient with fewer estimation errors.

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No datasets were generated or analyzed during the current study.

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Authors and Affiliations

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

    Elaheh Gavagsaz

  2. Department of Computer Engineering, Faculty of Engineering, Haliç University, Istanbul, 34060, Turkey

    Alireza Souri

Authors
  1. Elaheh Gavagsaz
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  2. Alireza Souri
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Contributions

Dr. Elaheh Gavagsaz wrote the main manuscript text and Prof. Alireza Souri prepared investigation on the results and edited the main text. All authors reviewed the manuscript.

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Correspondence to Alireza Souri.

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Cite this article

Gavagsaz, E., Souri, A. Triangle-induced and degree-wise sampling over large graphs in social networks. J Supercomput 81, 145 (2025). https://doi.org/10.1007/s11227-024-06613-9

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  • DOI: https://doi.org/10.1007/s11227-024-06613-9

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