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Relating Real and Synthetic Social Networks Through Centrality Measures

Authors Maria J. Blesa , Mihail Eduard Popa, Maria Serna

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Author Details

Maria J. Blesa
  • Computer Science Department, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
Mihail Eduard Popa
  • Barcelona School of Informatics, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
Maria Serna
  • Computer Science Department, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
  • Institute of Mathematics (IMTech), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain

Funding

M. Blesa and M. Serna acknowledge support by MICIN/AEI/10.13039/501100011033 under grant PID2020-112581GB-C21 (MOTION). M.E. Popa was funded by the Ministry of Education and Vocational Training (MEFP) with a student grant (Beca de colaboración, call 2020-2021).

Cite As Get BibTex

Maria J. Blesa, Mihail Eduard Popa, and Maria Serna. Relating Real and Synthetic Social Networks Through Centrality Measures. In 20th International Symposium on Experimental Algorithms (SEA 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 233, pp. 7:1-7:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022) https://doi.org/10.4230/LIPIcs.SEA.2022.7

Abstract

We perform here a comparative study on the behaviour of real and synthetic social networks with respect to a selection of nine centrality measures. Some of them are topology based (degree, closeness, betweenness), while others consider the relevance of the actors within the network (Katz, PageRank) or their ability to spread influence through it (Independent Cascade rank, Linear Threshold Rank). We run different experiments on synthetic social networks, with 1K, 10K, and 100K nodes, generated according to the Gaussian Random partition model, the stochastic block model, the LFR benchmark graph model and hyperbolic geometric graphs model. Some real social networks are also considered, with the aim of discovering how do they relate to the synthetic models in terms of centrality. Apart from usual statistical measures, we perform a correlation analysis between all the nine measures. Our results indicate that, in general, the correlation matrices of the different models scale nicely with size. Moreover, the correlation plots distinguish four categories that classify most of the real networks studied here. Those categories have a clear correspondence with particular configurations of the models for synthetic networks.

Subject Classification

ACM Subject Classification
  • Networks → Network algorithms
  • Networks → Network dynamics
Keywords
  • centrality measures
  • influence spread models
  • synthetic social networks

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Supplementary Materials

References

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